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{
"title": "Graph Theory Mastery: 100 MCQs",
"description": "A comprehensive set of 100 multiple-choice questions on graph theory fundamentals, algorithms, and applications.",
"questions": [
{
"id": 1,
"questionText": "In graph theory, what is a 'node' (or vertex)?",
"options": [
"A connection between two edges",
"A cycle in the graph",
"A fundamental unit representing an entity",
"A weight assigned to an edge"
],
"correctAnswerIndex": 2,
"explanation": "A node (or vertex) is the basic entity in a graph representing an object, point, or entity."
},
{
"id": 2,
"questionText": "What is an 'edge' in a graph?",
"options": [
"A connection between two nodes",
"A property of a node",
"The maximum degree of a node",
"A label of a graph"
],
"correctAnswerIndex": 0,
"explanation": "An edge represents a link or relationship between two nodes in a graph."
},
{
"id": 3,
"questionText": "Which of the following is a directed graph?",
"options": [
"A graph with weighted nodes only",
"A graph where edges have no direction",
"A graph where edges have a direction from one node to another",
"A graph with only cycles"
],
"correctAnswerIndex": 2,
"explanation": "In a directed graph, each edge has a direction pointing from one node to another."
},
{
"id": 4,
"questionText": "What is an adjacency matrix?",
"options": [
"A matrix storing node degrees only",
"A feature matrix for machine learning",
"A distance matrix between two graphs",
"A matrix representing edge connections between nodes"
],
"correctAnswerIndex": 3,
"explanation": "An adjacency matrix is a square matrix used to represent which nodes are connected to each other."
},
{
"id": 5,
"questionText": "What is a weighted graph?",
"options": [
"A graph where all nodes have labels",
"A graph with only one node",
"A graph with edges labeled with weights or costs",
"A graph that cannot be connected"
],
"correctAnswerIndex": 2,
"explanation": "In a weighted graph, each edge has an associated numerical value representing cost, distance, or strength."
},
{
"id": 6,
"questionText": "In an undirected graph, how are edges represented?",
"options": [
"Edges have a direction from one node to another",
"Edges are always weighted",
"Edges have no direction; connection is bidirectional",
"Edges must form a cycle"
],
"correctAnswerIndex": 2,
"explanation": "In an undirected graph, edges represent a two-way connection between nodes."
},
{
"id": 7,
"questionText": "What is the degree of a node in a graph?",
"options": [
"The number of edges connected to it",
"The sum of edge weights",
"The distance to the farthest node",
"The total number of nodes"
],
"correctAnswerIndex": 0,
"explanation": "The degree of a node is the count of edges connected to it."
},
{
"id": 8,
"questionText": "Which of the following best describes a path in a graph?",
"options": [
"The adjacency matrix of a graph",
"A cycle with repeated nodes",
"A node with maximum degree",
"A sequence of edges connecting a set of nodes"
],
"correctAnswerIndex": 3,
"explanation": "A path is a sequence of edges connecting nodes, without necessarily forming a cycle."
},
{
"id": 9,
"questionText": "What is a connected graph?",
"options": [
"A graph with no edges",
"A graph where there is a path between every pair of nodes",
"A graph where each node has the same degree",
"A graph where all nodes form cycles"
],
"correctAnswerIndex": 1,
"explanation": "A connected graph ensures that every node can be reached from any other node via some path."
},
{
"id": 10,
"questionText": "What is a cycle in a graph?",
"options": [
"The longest path in a graph",
"A path connecting exactly two nodes",
"A node with maximum degree",
"A path that starts and ends at the same node without repeating edges"
],
"correctAnswerIndex": 3,
"explanation": "A cycle is a path where the start and end nodes are the same, forming a closed loop."
},
{
"id": 11,
"questionText": "Which graph type allows multiple edges between the same nodes?",
"options": [
"Tree",
"Simple graph",
"Directed acyclic graph",
"Multigraph"
],
"correctAnswerIndex": 3,
"explanation": "A multigraph allows two or more edges between the same pair of nodes."
},
{
"id": 12,
"questionText": "What is a tree in graph theory?",
"options": [
"A connected acyclic graph",
"A graph with cycles",
"A disconnected graph",
"A graph with weighted edges only"
],
"correctAnswerIndex": 0,
"explanation": "A tree is a connected graph without cycles, often used in hierarchical structures."
},
{
"id": 13,
"questionText": "What is a leaf node in a tree?",
"options": [
"A node with exactly one child",
"A node with no edges",
"A node with maximum connections",
"A node with degree 1 (no children if root is counted)"
],
"correctAnswerIndex": 3,
"explanation": "A leaf node is a node at the end of a tree with no children (degree 1 if considering connections)."
},
{
"id": 14,
"questionText": "What is a directed acyclic graph (DAG) used for?",
"options": [
"Representing cycles",
"Representing hierarchical dependencies without loops",
"Representing undirected paths",
"Representing unconnected nodes"
],
"correctAnswerIndex": 1,
"explanation": "DAGs are used to model processes or dependencies where cycles are not allowed."
},
{
"id": 15,
"questionText": "Which data structure is most commonly used to represent sparse graphs efficiently?",
"options": [
"Heap",
"Adjacency list",
"Queue",
"Adjacency matrix"
],
"correctAnswerIndex": 1,
"explanation": "Adjacency lists store only existing edges, making them memory-efficient for sparse graphs."
},
{
"id": 16,
"questionText": "What is a bipartite graph?",
"options": [
"A tree with two levels",
"A graph with only weighted edges",
"A graph with two disconnected components",
"A graph whose nodes can be divided into two sets with edges only across sets"
],
"correctAnswerIndex": 3,
"explanation": "In a bipartite graph, nodes can be partitioned into two groups with edges only connecting nodes from different groups."
},
{
"id": 17,
"questionText": "Which algorithm finds the shortest path in a weighted graph?",
"options": [
"PageRank",
"Breadth-first search",
"Dijkstra's algorithm",
"Depth-first search"
],
"correctAnswerIndex": 2,
"explanation": "Dijkstra's algorithm calculates the shortest paths from a source node to all other nodes in a weighted graph."
},
{
"id": 18,
"questionText": "What does the adjacency list of a graph store?",
"options": [
"Each node and a list of nodes directly connected to it",
"Only the number of nodes",
"All possible paths in the graph",
"Only the degree of each node"
],
"correctAnswerIndex": 0,
"explanation": "Adjacency lists store each node along with a list of neighbors it is directly connected to."
},
{
"id": 19,
"questionText": "Which graph search algorithm uses a queue to explore nodes level by level?",
"options": [
"Depth-first search (DFS)",
"Breadth-first search (BFS)",
"Kruskal's algorithm",
"Dijkstra's algorithm"
],
"correctAnswerIndex": 1,
"explanation": "BFS explores neighbors level by level using a queue."
},
{
"id": 20,
"questionText": "Which graph search algorithm uses a stack or recursion to explore deep paths first?",
"options": [
"PageRank",
"Breadth-first search (BFS)",
"Prim's algorithm",
"Depth-first search (DFS)"
],
"correctAnswerIndex": 3,
"explanation": "DFS explores as far as possible along a branch before backtracking."
},
{
"id": 21,
"questionText": "What is the clustering coefficient in a graph?",
"options": [
"Measure of how strongly nodes are connected locally",
"Total number of cycles",
"Number of edges in the graph",
"Distance between two farthest nodes"
],
"correctAnswerIndex": 0,
"explanation": "The clustering coefficient measures the degree to which nodes tend to cluster together."
},
{
"id": 22,
"questionText": "Which property defines a complete graph?",
"options": [
"No edges",
"Only weighted edges",
"All nodes are connected to every other node",
"Contains exactly one cycle"
],
"correctAnswerIndex": 2,
"explanation": "In a complete graph, each node has an edge to every other node."
},
{
"id": 23,
"questionText": "What is the PageRank algorithm used for?",
"options": [
"Detecting cycles in a graph",
"Clustering nodes based on attributes",
"Ranking nodes in a network based on importance",
"Finding shortest paths"
],
"correctAnswerIndex": 2,
"explanation": "PageRank measures the importance of nodes based on connections, originally used for web pages."
},
{
"id": 24,
"questionText": "What is a strongly connected component in a directed graph?",
"options": [
"A subset of nodes where every node is reachable from every other node in the subset",
"A graph without cycles",
"An edge with maximum weight",
"A node with degree 1"
],
"correctAnswerIndex": 0,
"explanation": "In a strongly connected component, every node can reach every other node through directed edges."
},
{
"id": 25,
"questionText": "Which of the following is a spectral property of a graph?",
"options": [
"Eigenvalues of the adjacency or Laplacian matrix",
"Number of cycles",
"Edges connecting bipartite sets",
"Node degrees only"
],
"correctAnswerIndex": 0,
"explanation": "Graph spectra refer to eigenvalues of matrices representing the graph, like adjacency or Laplacian matrices."
},
{
"id": 26,
"questionText": "What is label propagation in graph-based learning?",
"options": [
"A way to spread node labels to neighbors for semi-supervised learning",
"A way to remove noisy nodes",
"A graph compression method",
"A way to sort nodes by degree"
],
"correctAnswerIndex": 0,
"explanation": "Label propagation spreads labels from labeled nodes to unlabeled nodes using graph connectivity."
},
{
"id": 27,
"questionText": "Which type of graph has no cycles at all?",
"options": [
"Tree",
"Multigraph",
"Directed cyclic graph",
"Complete graph"
],
"correctAnswerIndex": 0,
"explanation": "A tree is an acyclic connected graph, meaning it has no cycles."
},
{
"id": 28,
"questionText": "Which measure reflects how close a node is to all other nodes in a network?",
"options": [
"Betweenness centrality",
"Closeness centrality",
"PageRank",
"Degree centrality"
],
"correctAnswerIndex": 1,
"explanation": "Closeness centrality measures how quickly a node can reach all other nodes."
},
{
"id": 29,
"questionText": "What does the adjacency matrix entry A[i][j] = 1 signify in an unweighted graph?",
"options": [
"Node i has degree j",
"Node i and j are connected by an edge",
"Node j has no edges",
"Node i is unreachable from j"
],
"correctAnswerIndex": 1,
"explanation": "In an adjacency matrix, 1 indicates that there is an edge connecting node i to node j."
},
{
"id": 30,
"questionText": "Which method is used to detect communities in a graph?",
"options": [
"BFS and DFS only",
"Dijkstra's shortest path",
"Adjacency matrix sorting",
"Clustering algorithms like Girvan–Newman or Louvain"
],
"correctAnswerIndex": 3,
"explanation": "Community detection algorithms partition a graph into clusters or modules of closely connected nodes."
},
{
"id": 31,
"questionText": "What is the main idea behind spectral clustering in graphs?",
"options": [
"Clustering nodes based on shortest path lengths",
"Assigning random clusters",
"Using node degrees only",
"Using eigenvalues and eigenvectors of graph Laplacian to cluster nodes"
],
"correctAnswerIndex": 3,
"explanation": "Spectral clustering uses the Laplacian matrix's eigenvectors to project nodes into a lower-dimensional space and perform clustering."
},
{
"id": 32,
"questionText": "What does the Laplacian matrix of a graph represent?",
"options": [
"Adjacency matrix plus identity matrix",
"Shortest paths",
"Edge weights only",
"Degree matrix minus adjacency matrix"
],
"correctAnswerIndex": 3,
"explanation": "The Laplacian matrix L = D - A, where D is the degree matrix and A is adjacency matrix, captures the graph structure for spectral methods."
},
{
"id": 33,
"questionText": "In label propagation, how are unlabeled nodes assigned labels?",
"options": [
"Using node degree only",
"Randomly",
"Based on the most frequent label among neighbors iteratively",
"Using BFS distance"
],
"correctAnswerIndex": 2,
"explanation": "Label propagation spreads labels iteratively from labeled nodes to neighbors based on majority voting."
},
{
"id": 34,
"questionText": "What is PageRank primarily used for in graph-based methods?",
"options": [
"Ranking nodes by importance based on connections",
"Finding shortest paths",
"Clustering communities",
"Measuring betweenness centrality"
],
"correctAnswerIndex": 0,
"explanation": "PageRank assigns importance to nodes based on their connections and the importance of connecting nodes."
},
{
"id": 35,
"questionText": "What is betweenness centrality?",
"options": [
"Number of edges a node has",
"Number of shortest paths passing through a node",
"Eigenvalue of the adjacency matrix",
"Average distance from a node to all others"
],
"correctAnswerIndex": 1,
"explanation": "Betweenness centrality measures how often a node lies on the shortest paths between other nodes."
},
{
"id": 36,
"questionText": "Which method is commonly used to partition large graphs into communities?",
"options": [
"Girvan–Newman algorithm",
"Dijkstra's algorithm",
"Breadth-first search only",
"DFS only"
],
"correctAnswerIndex": 0,
"explanation": "Girvan–Newman removes edges with high betweenness iteratively to detect communities."
},
{
"id": 37,
"questionText": "What does the modularity score measure?",
"options": [
"Shortest path length",
"Number of nodes in graph",
"Density of edges within clusters compared to random graph",
"Total edge weight"
],
"correctAnswerIndex": 2,
"explanation": "Modularity measures the strength of division of a network into clusters or communities."
},
{
"id": 38,
"questionText": "In semi-supervised learning on graphs, what is the main advantage?",
"options": [
"Ignores unlabeled nodes",
"Only uses labeled data",
"Randomly assigns labels",
"Uses graph structure to propagate labels from labeled to unlabeled nodes"
],
"correctAnswerIndex": 3,
"explanation": "Graph structure allows label propagation to improve predictions using unlabeled nodes."
},
{
"id": 39,
"questionText": "Which property of a graph is used in random walk-based algorithms?",
"options": [
"Degree distribution ignored",
"Number of triangles",
"Edge weights and connectivity",
"Node labels only"
],
"correctAnswerIndex": 2,
"explanation": "Random walks traverse nodes using edge connectivity and sometimes edge weights, capturing network structure."
},
{
"id": 40,
"questionText": "What is the main goal of community detection algorithms?",
"options": [
"Identify groups of nodes with dense internal connections and sparse external connections",
"Find shortest paths",
"Delete low-degree nodes",
"Rank nodes by degree"
],
"correctAnswerIndex": 0,
"explanation": "Community detection identifies clusters where nodes are more connected internally than externally."
},
{
"id": 41,
"questionText": "Which algorithm is used to find connected components in a graph?",
"options": [
"Spectral clustering",
"DFS or BFS traversal",
"PageRank",
"Dijkstra's algorithm"
],
"correctAnswerIndex": 1,
"explanation": "DFS or BFS can explore each component to identify connected nodes."
},
{
"id": 42,
"questionText": "What is the difference between transductive and inductive graph-based learning?",
"options": [
"Transductive predicts labels for existing nodes only; inductive generalizes to unseen nodes",
"Both are the same",
"Inductive predicts for existing nodes only; transductive generalizes",
"Inductive ignores graph structure"
],
"correctAnswerIndex": 0,
"explanation": "Transductive learning focuses on predicting labels for the given graph, while inductive models can predict for new nodes."
},
{
"id": 43,
"questionText": "What is a random walk with restart (RWR) used for?",
"options": [
"Calculating node degree",
"Ranking nodes based on proximity to a seed node",
"Finding shortest paths",
"Clustering nodes randomly"
],
"correctAnswerIndex": 1,
"explanation": "RWR simulates walks that frequently return to a seed node to compute node proximity scores."
},
{
"id": 44,
"questionText": "Which method uses eigenvectors of the Laplacian for clustering?",
"options": [
"Label propagation",
"Spectral clustering",
"PageRank",
"DFS"
],
"correctAnswerIndex": 1,
"explanation": "Spectral clustering projects nodes using Laplacian eigenvectors to a lower-dimensional space for clustering."
},
{
"id": 45,
"questionText": "What is assortativity in a network?",
"options": [
"Tendency of nodes to connect to similar nodes",
"Node clustering coefficient",
"Average path length",
"Random connection of nodes"
],
"correctAnswerIndex": 0,
"explanation": "Assortativity measures the preference of nodes to attach to others with similar properties, like degree."
},
{
"id": 46,
"questionText": "In a weighted graph, what does the edge weight usually represent?",
"options": [
"Node label only",
"Number of nodes",
"Node color",
"Strength, cost, or distance between nodes"
],
"correctAnswerIndex": 3,
"explanation": "Edge weights quantify connection strength or other metrics like cost or similarity."
},
{
"id": 47,
"questionText": "Which graph structure is commonly used for recommendation systems?",
"options": [
"Bipartite graphs linking users and items",
"Trees only",
"Directed cycles only",
"Complete graphs only"
],
"correctAnswerIndex": 0,
"explanation": "User-item interactions are modeled as bipartite graphs in recommendation systems."
},
{
"id": 48,
"questionText": "Which centrality measure identifies nodes that are critical bridges in the network?",
"options": [
"Betweenness centrality",
"Degree centrality",
"PageRank",
"Closeness centrality"
],
"correctAnswerIndex": 0,
"explanation": "Nodes with high betweenness centrality often serve as bridges connecting different parts of the graph."
},
{
"id": 49,
"questionText": "Which technique can reduce dimensionality of node representations for graph-based learning?",
"options": [
"Adjacency list sorting",
"DFS",
"Graph embedding methods like node2vec or DeepWalk",
"BFS"
],
"correctAnswerIndex": 2,
"explanation": "Graph embeddings encode nodes into low-dimensional vectors preserving structural properties."
},
{
"id": 50,
"questionText": "What is the primary goal of semi-supervised learning on graphs?",
"options": [
"Compute shortest paths only",
"Ignore unlabeled nodes",
"Predict labels for unlabeled nodes using graph structure and few labeled nodes",
"Assign random labels"
],
"correctAnswerIndex": 2,
"explanation": "Semi-supervised graph learning propagates label information from labeled to unlabeled nodes using the graph topology."
},
{
"id": 51,
"questionText": "Which algorithm propagates labels iteratively based on neighbor majority votes?",
"options": [
"Random walk without restart",
"Spectral clustering",
"Label propagation",
"Dijkstra's algorithm"
],
"correctAnswerIndex": 2,
"explanation": "Label propagation iteratively assigns labels to unlabeled nodes based on their neighbors' labels."
},
{
"id": 52,
"questionText": "Which property defines a scale-free network?",
"options": [
"All nodes have the same degree",
"Graph has no cycles",
"Node degrees follow a power-law distribution",
"Graph is fully connected"
],
"correctAnswerIndex": 2,
"explanation": "Scale-free networks have a few highly connected hubs and many nodes with low degree, following a power-law."
},
{
"id": 53,
"questionText": "Which graph algorithm is used to find a minimum spanning tree?",
"options": [
"DFS",
"Kruskal's or Prim's algorithm",
"BFS",
"PageRank"
],
"correctAnswerIndex": 1,
"explanation": "Kruskal's and Prim's algorithms compute a tree connecting all nodes with minimal total edge weight."
},
{
"id": 54,
"questionText": "Which measure indicates a node’s influence based on its connections to important nodes?",
"options": [
"Degree centrality",
"PageRank",
"Closeness centrality",
"Betweenness centrality"
],
"correctAnswerIndex": 1,
"explanation": "PageRank ranks nodes higher if they are linked by other important nodes."
},
{
"id": 55,
"questionText": "Which method finds node communities by removing edges with high betweenness?",
"options": [
"Spectral clustering",
"Girvan–Newman algorithm",
"Label propagation",
"DFS"
],
"correctAnswerIndex": 1,
"explanation": "Girvan–Newman iteratively removes high-betweenness edges to reveal communities."
},
{
"id": 56,
"questionText": "What is conductance in community detection?",
"options": [
"Node degree",
"Ratio of external edges to total edges in a cluster",
"Shortest path length",
"Number of cycles"
],
"correctAnswerIndex": 1,
"explanation": "Conductance measures how well a cluster is separated from the rest of the graph."
},
{
"id": 57,
"questionText": "Which algorithm is suitable for link prediction in social networks?",
"options": [
"Graph embedding methods like node2vec",
"Shortest path only",
"DFS",
"Prim's algorithm"
],
"correctAnswerIndex": 0,
"explanation": "Graph embeddings capture node similarities and structures for predicting missing links."
},
{
"id": 58,
"questionText": "What is the main use of a random walk in graph-based learning?",
"options": [
"Delete unconnected nodes",
"Identify cycles",
"Compute only node degrees",
"Capture local and global graph structure for node ranking or embeddings"
],
"correctAnswerIndex": 3,
"explanation": "Random walks traverse the graph probabilistically to capture structural information for algorithms."
},
{
"id": 59,
"questionText": "Which metric measures the average shortest distance between all pairs of nodes?",
"options": [
"Degree centrality",
"Average path length",
"Betweenness centrality",
"Eigenvector centrality"
],
"correctAnswerIndex": 1,
"explanation": "Average path length indicates how many steps, on average, it takes to reach from one node to another."
},
{
"id": 60,
"questionText": "Which graph type is used to model social interactions or collaboration networks?",
"options": [
"Complete graphs only",
"Directed acyclic graphs only",
"Trees only",
"Undirected and weighted graphs"
],
"correctAnswerIndex": 3,
"explanation": "Social and collaboration networks often have undirected edges and sometimes weighted edges representing interaction strength."
},
{
"id": 61,
"questionText": "Which property ensures that a graph has no cycles?",
"options": [
"Degree centrality",
"Connectedness",
"Acyclic property",
"Closeness centrality"
],
"correctAnswerIndex": 2,
"explanation": "Acyclic graphs (like trees) do not contain cycles."
},
{
"id": 62,
"questionText": "What is a hub in a network?",
"options": [
"A node with very high degree",
"A node with zero degree",
"A leaf node in a tree",
"A node in a cycle only"
],
"correctAnswerIndex": 0,
"explanation": "Hubs are highly connected nodes in networks, often playing key roles."
},
{
"id": 63,
"questionText": "Which method can detect communities in large networks efficiently?",
"options": [
"Louvain method",
"Prim's algorithm",
"DFS",
"BFS"
],
"correctAnswerIndex": 0,
"explanation": "The Louvain method is scalable and identifies communities by optimizing modularity."
},
{
"id": 64,
"questionText": "Which algorithm is suitable for hierarchical clustering on graphs?",
"options": [
"Spectral clustering using eigenvectors",
"DFS",
"PageRank",
"Breadth-first search"
],
"correctAnswerIndex": 0,
"explanation": "Spectral methods can be used to generate hierarchical clusters using eigenvectors of the Laplacian."
},
{
"id": 65,
"questionText": "Which property is used to identify structural equivalence of nodes?",
"options": [
"Shortest path",
"Graph diameter",
"Similarity of neighbors and connections",
"Node degree only"
],
"correctAnswerIndex": 2,
"explanation": "Structural equivalence identifies nodes with similar connection patterns in the network."
},
{
"id": 66,
"questionText": "Which method can learn continuous vector representations of nodes?",
"options": [
"DFS",
"Graph embedding methods like node2vec and DeepWalk",
"PageRank only",
"BFS"
],
"correctAnswerIndex": 1,
"explanation": "Graph embeddings encode nodes into vectors that capture graph structure for machine learning tasks."
},
{
"id": 67,
"questionText": "Which metric helps identify central nodes that are closest to all other nodes?",
"options": [
"Degree centrality",
"Betweenness centrality",
"Closeness centrality",
"Eigenvector centrality"
],
"correctAnswerIndex": 2,
"explanation": "Closeness centrality measures how near a node is to all others in terms of shortest paths."
},
{
"id": 68,
"questionText": "Which approach is used to find influential nodes for information spread?",
"options": [
"Centrality measures (betweenness, degree, PageRank)",
"Shortest path only",
"DFS only",
"Random node selection"
],
"correctAnswerIndex": 0,
"explanation": "Influential nodes are identified using centrality metrics to maximize information diffusion."
},
{
"id": 69,
"questionText": "Which method in graph-based ML uses labeled nodes to infer labels for unlabeled nodes using graph structure?",
"options": [
"DFS",
"Semi-supervised learning on graphs",
"Minimum spanning tree",
"BFS"
],
"correctAnswerIndex": 1,
"explanation": "Semi-supervised learning leverages graph structure to propagate labels from labeled to unlabeled nodes."
},
{
"id": 70,
"questionText": "Which algorithm combines random walks and node embeddings for link prediction?",
"options": [
"DFS",
"node2vec",
"Prim's algorithm",
"Breadth-first search"
],
"correctAnswerIndex": 1,
"explanation": "node2vec uses biased random walks to generate node embeddings suitable for link prediction."
},
{
"id": 71,
"questionText": "In a social network graph, you want to identify potential influencers for a marketing campaign. Which metric would you prioritize?",
"options": [
"Node degree",
"Graph diameter",
"Clustering coefficient",
"Betweenness centrality"
],
"correctAnswerIndex": 3,
"explanation": "Betweenness centrality identifies nodes that act as bridges in the network, which can maximize influence spread."
},
{
"id": 72,
"questionText": "A financial transaction network is modeled as a graph. Suspicious patterns indicate potential fraud. Which approach is most suitable?",
"options": [
"PageRank only",
"Shortest path computation",
"Community detection to find unusual clusters",
"DFS traversal"
],
"correctAnswerIndex": 2,
"explanation": "Community detection can reveal groups of transactions that are unusually connected, indicating potential fraudulent activity."
},
{
"id": 73,
"questionText": "In a citation network, how can you identify influential research papers?",
"options": [
"Count node degrees only",
"Use BFS from each node",
"Use PageRank to rank nodes based on incoming citations",
"Randomly sample papers"
],
"correctAnswerIndex": 2,
"explanation": "PageRank ranks nodes based on both quantity and quality of citations, identifying influential papers."
},
{
"id": 74,
"questionText": "You have a user-item bipartite graph for recommendations. Which method can help predict new items for users?",
"options": [
"Graph diameter",
"Graph embeddings and link prediction",
"DFS",
"Clustering coefficient only"
],
"correctAnswerIndex": 1,
"explanation": "Graph embeddings capture structural similarity between nodes, which helps predict potential user-item links."
},
{
"id": 75,
"questionText": "In a protein-protein interaction network, you want to find critical proteins. Which centrality measure is most appropriate?",
"options": [
"Degree centrality",
"Betweenness centrality",
"Closeness centrality",
"PageRank"
],
"correctAnswerIndex": 1,
"explanation": "Proteins acting as bridges (high betweenness) are critical for biological processes and network stability."
},
{
"id": 76,
"questionText": "In a transportation network, you want to identify bottlenecks. Which property should you analyze?",
"options": [
"Betweenness centrality",
"Node degree",
"Closeness centrality",
"Random walk probability"
],
"correctAnswerIndex": 0,
"explanation": "Nodes with high betweenness often lie on the most traveled paths, indicating bottlenecks."
},
{
"id": 77,
"questionText": "A communication network shows some nodes receiving delayed messages. Which analysis can help locate critical nodes causing delay?",
"options": [
"Betweenness centrality and shortest path analysis",
"DFS traversal",
"Node degree only",
"PageRank only"
],
"correctAnswerIndex": 0,
"explanation": "Nodes with high betweenness affect many paths, causing delays; analyzing shortest paths helps identify them."
},
{
"id": 78,
"questionText": "You want to detect communities in a large online forum network with millions of users. Which algorithm is efficient?",
"options": [
"BFS",
"Girvan–Newman",
"DFS",
"Louvain method"
],
"correctAnswerIndex": 3,
"explanation": "The Louvain method scales well for large networks and detects communities efficiently by optimizing modularity."
},
{
"id": 79,
"questionText": "In a recommendation system graph, you want to suggest items similar to a user’s previous interactions. Which method is suitable?",
"options": [
"Graph embeddings like node2vec or DeepWalk",
"Random node selection",
"DFS",
"Shortest path only"
],
"correctAnswerIndex": 0,
"explanation": "Graph embeddings capture structural similarities between nodes, enabling accurate recommendations."
},
{
"id": 80,
"questionText": "A transportation network graph needs redundancy for critical nodes. How do you identify nodes requiring backup routes?",
"options": [
"Nodes in cycles only",
"High degree nodes only",
"High betweenness centrality nodes",
"Leaf nodes"
],
"correctAnswerIndex": 2,
"explanation": "High betweenness nodes are critical for network flow; backup routes increase resilience."
},
{
"id": 81,
"questionText": "In a web hyperlink network, how can you identify authoritative pages?",
"options": [
"Use BFS traversal",
"Use PageRank to measure link importance",
"Use shortest path only",
"Use degree centrality only"
],
"correctAnswerIndex": 1,
"explanation": "PageRank scores pages higher if linked by other important pages, identifying authoritative sources."
},
{
"id": 82,
"questionText": "You have a knowledge graph for healthcare. How can you infer relationships between entities that are not directly linked?",
"options": [
"Use graph embeddings for link prediction",
"Closeness centrality only",
"DFS traversal",
"Random node selection"
],
"correctAnswerIndex": 0,
"explanation": "Graph embeddings encode structural patterns, enabling prediction of missing or hidden links."
},
{
"id": 83,
"questionText": "A social network shows isolated clusters. You want to identify nodes that can connect clusters efficiently. Which metric helps?",
"options": [
"PageRank",
"Degree centrality",
"Closeness centrality",
"Betweenness centrality"
],
"correctAnswerIndex": 3,
"explanation": "Nodes with high betweenness connect clusters and facilitate communication between them."
},
{
"id": 84,
"questionText": "In a citation graph, you want to find emerging influential papers. Which approach is suitable?",
"options": [
"Combine node age with PageRank scores",
"DFS traversal",
"Degree centrality only",
"Random selection"
],
"correctAnswerIndex": 0,
"explanation": "New papers with increasing citations and high PageRank can indicate emerging influential research."
},
{
"id": 85,
"questionText": "In a collaboration network, how can you identify tightly connected research groups?",
"options": [
"Use community detection algorithms like Louvain",
"BFS only",
"DFS only",
"Random sampling"
],
"correctAnswerIndex": 0,
"explanation": "Community detection groups researchers who frequently collaborate, revealing tightly connected clusters."
},
{
"id": 86,
"questionText": "In a fraud detection network, suspicious activity forms unusual patterns. Which method can reveal these?",
"options": [
"PageRank only",
"DFS traversal",
"Degree centrality only",
"Anomaly detection using subgraph patterns"
],
"correctAnswerIndex": 3,
"explanation": "Unusual subgraph patterns indicate anomalies and possible fraudulent activities."
},
{
"id": 87,
"questionText": "You have a product co-purchase network. How can you recommend products not yet purchased by a user?",
"options": [
"BFS only",
"PageRank only",
"DFS traversal",
"Use link prediction with graph embeddings"
],
"correctAnswerIndex": 3,
"explanation": "Link prediction predicts potential links between users and products using graph embeddings."
},
{
"id": 88,
"questionText": "In a citation network, you want to visualize influential clusters. Which method is suitable?",
"options": [
"Degree centrality only",
"Community detection followed by graph layout visualization",
"Shortest path only",
"DFS traversal"
],
"correctAnswerIndex": 1,
"explanation": "Detecting communities first and then visualizing them highlights influential clusters effectively."
},
{
"id": 89,
"questionText": "In a disease spread network, how can you identify super-spreaders?",
"options": [
"Nodes with low degree",
"Leaf nodes",
"Random nodes",
"Nodes with high degree and betweenness centrality"
],
"correctAnswerIndex": 3,
"explanation": "Super-spreaders have many connections and lie on critical paths, making degree and betweenness useful metrics."
},
{
"id": 90,
"questionText": "In a social media network, how can you detect echo chambers?",
"options": [
"Shortest path only",
"DFS only",
"Degree centrality only",
"Community detection and analyzing internal vs. external edges"
],
"correctAnswerIndex": 3,
"explanation": "Echo chambers are tightly-knit communities with dense internal connections and few external links."
},
{
"id": 91,
"questionText": "You want to predict missing friendships in a social network. Which technique is most effective?",
"options": [
"BFS traversal only",
"Clustering coefficient only",
"Graph embedding and link prediction",
"DFS traversal"
],
"correctAnswerIndex": 2,
"explanation": "Graph embeddings capture structural patterns to predict probable new links between nodes."
},
{
"id": 92,
"questionText": "In a supply chain network, which nodes should be prioritized for risk mitigation?",
"options": [
"Nodes in cycles only",
"Leaf nodes only",
"Nodes with high betweenness centrality",
"Nodes with low degree"
],
"correctAnswerIndex": 2,
"explanation": "Nodes critical to network flow (high betweenness) are key points for risk management."
},
{
"id": 93,
"questionText": "Which method can identify overlapping communities in a network?",
"options": [
"Clique percolation method",
"DFS only",
"Shortest path only",
"PageRank only"
],
"correctAnswerIndex": 0,
"explanation": "Clique percolation detects overlapping communities by analyzing shared cliques among nodes."
},
{
"id": 94,
"questionText": "In a knowledge graph, how can you infer new facts from existing relationships?",
"options": [
"BFS traversal only",
"Use graph embeddings and reasoning techniques",
"Degree centrality only",
"DFS traversal only"
],
"correctAnswerIndex": 1,
"explanation": "Graph embeddings combined with reasoning allow inference of likely but missing relationships."
},
{
"id": 95,
"questionText": "In a network with multiple hubs, how can you identify potential vulnerabilities?",
"options": [
"Leaf nodes",
"Target hubs with high degree and betweenness centrality",
"Random nodes",
"Nodes in cycles only"
],
"correctAnswerIndex": 1,
"explanation": "Removing critical hubs can disrupt network connectivity, revealing vulnerabilities."
},
{
"id": 96,
"questionText": "For a recommendation system, which graph approach captures both user similarity and item similarity?",
"options": [
"Graph embeddings with random walks",
"PageRank only",
"DFS traversal only",
"Shortest path only"
],
"correctAnswerIndex": 0,
"explanation": "Graph embeddings capture structural similarity of both users and items for accurate recommendations."
},
{
"id": 97,
"questionText": "In a network of roads, which method can identify critical junctions for emergency planning?",
"options": [
"DFS only",
"Leaf nodes",
"High betweenness centrality nodes",
"Closeness centrality only"
],
"correctAnswerIndex": 2,
"explanation": "Junctions on many shortest paths are critical; betweenness centrality identifies them."
},
{
"id": 98,
"questionText": "In a scientific collaboration network, how can you identify influential research groups?",
"options": [
"Use community detection and measure centrality of group nodes",
"Shortest path only",
"PageRank only",
"DFS only"
],
"correctAnswerIndex": 0,
"explanation": "Communities reveal research groups; centrality within communities identifies influence."
},
{
"id": 99,
"questionText": "In a transaction network, which approach can detect rings of fraudulent activity?",
"options": [
"DFS only",
"Degree centrality only",
"BFS only",
"Subgraph anomaly detection"
],
"correctAnswerIndex": 3,
"explanation": "Detecting unusual subgraph patterns can reveal rings of fraudulent transactions."
},
{
"id": 100,
"questionText": "In a disease spread network, you want to immunize individuals to minimize outbreaks. Which strategy is most effective?",
"options": [
"Randomly select nodes",
"Target nodes with highest degree or betweenness centrality",
"Target nodes with lowest degree",
"Target leaf nodes only"
],
"correctAnswerIndex": 1,
"explanation": "Immunizing nodes that are highly connected or act as bridges prevents the disease from spreading efficiently through the network."
}
]
}
|