Graph mining MCQs December 19, 2025November 19, 2024 by u930973931_answers 1 min Score: 0 Attempted: 0/1 Subscribe 1. 1. What is graph mining used for?2. Which of the following is a key component of a graph?3. What does graph clustering aim to achieve in graph mining?4. Which of the following algorithms is commonly used for community detection in graph mining?5. What does the PageRank algorithm do in graph mining?6. In graph mining, graph traversal is used to:7. What is a frequent subgraph in graph mining?8. What is graph classification in graph mining?9. What is a spanning tree in graph mining?10. Which of the following techniques is used to find similar graphs in graph mining?11. In graph mining, centrality measures are used to:12. What does graph anomaly detection aim to identify?13. In graph mining, what is a Markov Chain used for?14. What is graph embedding in the context of graph mining?15. In graph mining, what does link prediction focus on? (A) Analyzing numerical data in tabular form B. Analyzing relationships and patterns in graph-structured data C. Storing and indexing relational databases D. Reducing the dimensionality of image data Answer: B (Graph mining is focused on analyzing relationships and patterns in graph-structured data, such as social networks, web graphs, or molecular structures.) (A) Nodes (vertices) and edges (links) B. Rows and columns C. Data points and time intervals D. Records and fields Answer: A (A graph consists of nodes (vertices), which represent entities, and edges (links), which represent relationships or connections between these entities.) (A) Grouping nodes in the graph based on similarity or proximity B. Reducing the number of edges in the graph C. Classifying nodes based on predefined labels D. Finding the shortest path between two nodes Answer: A (Graph clustering aims to group similar or closely related nodes together, often used to find communities or subgraphs within larger graphs.) (A) K-means clustering B. PageRank algorithm C. Louvain method D. Naive Bayes classifier Answer: C (The Louvain method is a popular algorithm for detecting communities within large graphs, optimizing modularity to find densely connected groups of nodes.) (A) Classifies nodes in a graph B. Detects communities within the graph C. Determines the importance of nodes in a directed graph D. Groups nodes based on proximity Answer: C (PageRank is used to determine the importance or centrality of nodes in a directed graph, such as web pages in the context of search engines.) (A) Find the shortest path between nodes B. Identify all the nodes and edges in the graph C. Detect cycles in the graph D. Cluster nodes based on similarity Answer: B (Graph traversal refers to visiting and processing all the nodes and edges in the graph, often used as a preprocessing step in various graph mining tasks.) (A) A subgraph that appears infrequently in the graph B. A subgraph that contains cycles C. A subgraph that appears frequently across multiple graphs in a dataset D. A subgraph with the highest number of edges Answer: C (A frequent subgraph is a subgraph that appears frequently in a collection of graphs, often used in applications like pattern mining in molecular graphs or social network analysis.) (A) Sorting nodes in the graph based on their importance B. Categorizing entire graphs into predefined classes based on their structure or properties C. Clustering nodes into communities D. Detecting anomalies or outliers in the graph Answer: B (Graph classification involves categorizing entire graphs based on their structure, properties, or features, such as classifying chemical compounds based on their molecular structure.) (A) A subgraph that connects all the nodes with the minimum number of edges B. A subgraph that contains all the edges of the graph C. A graph where all nodes are isolated from each other D. A graph with no cycles Answer: A (A spanning tree is a subgraph that includes all the nodes of the original graph with the minimum number of edges and no cycles, ensuring connectivity.) (A) Shortest path algorithms B. Graph isomorphism algorithms C. K-means clustering D. Decision tree classifiers Answer: B (Graph isomorphism algorithms are used to determine whether two graphs are structurally identical, helping to find similar graphs in graph mining.) (A) Identify nodes that are most important or influential in a graph B. Detect cycles in a graph C. Classify graphs into categories D. Group nodes based on similarity Answer: A (Centrality measures identify nodes in a graph that are most important or influential, often used to find key nodes in social networks or web graphs.) (A) Nodes that are isolated from the rest of the graph B. Subgraphs that appear infrequently in the graph C. Outlier nodes or edges that deviate from normal patterns in the graph D. The shortest path between two nodes Answer: C (Graph anomaly detection aims to identify unusual or outlier nodes or edges that deviate from expected patterns, useful for fraud detection or identifying rare events in graphs.) (A) To detect communities in graphs B. To analyze the transitions between different states or nodes in a graph C. To classify nodes into categories D. To find the shortest path in a graph Answer: B (A Markov Chain is used to model the transitions between different states or nodes in a graph, often applied in algorithms like PageRank or modeling random walks.) (A) Transforming a graph into a lower-dimensional vector space to make it easier to analyze B. Storing graph data in relational databases C. Grouping similar graphs together D. Applying a clustering algorithm to graph nodes Answer: A (Graph embedding refers to mapping a graph or its nodes into a continuous vector space, which makes it easier to apply machine learning techniques and analyze graph properties.) (A) Identifying new connections or links that may form between nodes in the future B. Clustering nodes based on similarity C. Classifying nodes into different categories D. Finding the most important node in the graph Answer: A (Link prediction aims to predict potential future links or relationships between nodes based on existing patterns in the graph, widely used in social network analysis or recommender systems.)