MCQs

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Overall MCQs for Reference

Which of the following best describes link prediction in AI?
a. Identifying missing or future connections in a network
b. Clustering nodes with similar features
c. Classifying nodes into categories
d. Finding shortest paths between nodes
Answer: asciencedirect+2
Which technique is commonly used for unsupervised link prediction?
a. Decision Trees
b. Similarity-based metrics
c. K-means clustering
d. Random Forest
Answer: bwikipedia+1
Which metric measures the number of shared neighbors between node pairs in a graph?
a. Jaccard Index
b. Adamic/Adar Score
c. Common Neighbors
d. Hamming Distance
Answer: cslideshare+2
In bipartite networks, link prediction focuses on:
a. Connecting nodes from different partitions
b. Predicting self-loops
c. Adding weights to loops
d. Reducing network density
Answer: asciencedirect+1
Which machine learning approach frames link prediction as a binary classification problem?
a. Supervised learning
b. Unsupervised learning
c. Reinforcement learning
d. Clustering
Answer: aaionlinecourse+2
The Preferential Attachment score in link prediction is calculated based on:
a. Product of node degrees
b. Sum of path lengths
c. Maximum degree in network
d. Average clustering coefficient
Answer: awikipedia+2
Which algorithm is designed to predict links using node feature similarity and structural features combined?
a. K-means
b. Graph neural networks
c. K-nearest neighbors
d. Decision tree
Answer: bmilvus+1
What does link prediction in knowledge graphs aim to uncover?
a. Potential relationships not explicitly stated
b. Node labels
c. Feature selection bias
d. Decision boundaries
Answer: amilvus+2
Which index normalizes shared neighbors by the total number of neighbors from both nodes?
a. Dice coefficient
b. Katz Index
c. Jaccard Coefficient
d. Adamic/Adar Score
Answer: cslideshare+1
The Adamic/Adar Index penalizes:
a. Common neighbors with many connections
b. Distant neighbors
c. High-degree nodes only
d. Direct node pairs
Answer: awikipedia+1
Resource Allocation Index is similar to Adamic/Adar but uses:
a. Inverse degree
b. Logarithm of degree
c. Squared degree
d. Degree centrality
Answer: aslideshare+1
In dynamic networks, link prediction incorporates which factor?
a. Time
b. Node embeddings only
c. Random noise
d. Fixed structure
Answer: asciencedirect+1
Which feature can modern link prediction models combine to improve accuracy?
a. Node and edge attributes
b. Fixed label sets
c. Only topological data
d. Image data only
Answer: amilvus+1
What is a potential application of link prediction?
a. Social network friend suggestions
b. Binary search optimization
c. Unsupervised data augmentation
d. Image segmentation
Answer: aaionlinecourse+2
Matrix factorization methods for link prediction mainly use:
a. Latent space representations
b. Raw edge lists
c. Label encoding
d. Time series only
Answer: asciencedirect+1
Why are local similarity indices used in link prediction?
a. Low computational complexity
b. Only work on directed graphs
c. Require supervised training
d. Useful only for node classification
Answer: awikipedia+1
In probabilistic link prediction, algorithms estimate:
a. The likelihood of link formation
b. Sampling weights
c. Random feature subsets
d. Node label distributions
Answer: aaionlinecourse+1
Which machine learning model is commonly used for supervised link prediction?
a. Logistic regression
b. K-means
c. RNN
d. LDA
Answer: aaionlinecourse+1
Collective link prediction approaches aim to:
a. Jointly model all potential links
b. Predict single edges at a time
c. Only use node labels
d. Minimize node feature variance
Answer: awikipedia
Which of the following is NOT a heuristic-based link prediction method?
a. Preferential attachment
b. Common neighbors
c. Decision trees
d. Resource allocation
Answer: csciencedirect+1
The random walk approach for link prediction uses:
a. Path probabilities
b. Node communities
c. Degree sequences only
d. Static edge lists
Answer: aslideshare+1
Which graph structure does the link prediction problem model?
a. Nodes and edges
b. Only edges
c. Only adjacency matrices
d. Hyperplanes
Answer: asciencedirect+1
“AUC” in link prediction evaluation stands for:
a. Area under the curve
b. Average unique connections
c. Actual user count
d. Active user clustering
Answer: anature+1
The Katz index considers:
a. All possible paths between two nodes
b. Only immediate neighbors
c. Path length = 1 only
d. Node degrees only
Answer: aslideshare+1
Which type of network has links with direction included?
a. Directed networks
b. Bipartite networks
c. Heterogeneous networks
d. Erased networks
Answer: awikipedia+1
What is the main weakness of the Common Neighbors method?
a. Ignores the relative number of neighbors
b. High computational complexity
c. Cannot be used in undirected graphs
d. Only works for supervised learning
Answer: aslideshare+1
Graph embedding methods encode:
a. Nodes (and sometimes edges) into continuous vectors
b. Node colors only
c. Only adjacency matrix diagonal
d. Static labels
Answer: amilvus+1
Which method is often used for link prediction in very large networks due to efficiency?
a. Local similarity indices
b. Deep learning alone
c. Ablation studies
d. Manual annotation
Answer: aonlinelibrary.wiley+2
In link prediction, “homogeneous” networks contain:
a. Only one type of node and link
b. Different node types
c. Multiple link types
d. No links
Answer: awikipedia
Temporal link prediction involves:
a. Predicting links as networks evolve over time
b. Only static analysis
c. Exclusive use of K-means
d. Label propagation
Answer: auwindsor+1
Supervised link prediction models are usually evaluated with:
a. ROC curves
b. Loss functions for regression
c. Silhouette scores
d. Clustering accuracy
Answer: anature+1
What increases link prediction difficulty in real-world social networks?
a. High sparsity
b. Dense network only
c. Predicted nodes only
d. Only static data
Answer: aonlinelibrary.wiley+1
Which method generalizes link prediction to networks with different node and edge types?
a. Heterogeneous network models
b. Homogeneous clustering
c. Static embedding
d. Binary search trees
Answer: asciencedirect+1
Which technique allows link prediction to integrate textual or attribute information with structural data?
a. Content-based methods
b. Label encoding
c. KNN clustering
d. Edge coloring
Answer: amilvus+1
In biological networks, link prediction can be used to predict:
a. Protein-protein interactions
b. Node embeddings
c. Graph coloring
d. Labeling sequences
Answer: asciencedirect+1
Which neural network architecture is commonly used for learning complex node relationships for link prediction?
a. Graph neural networks
b. CNN
c. RNN
d. LSTM
Answer: aaionlinecourse+1
Link prediction in recommendation systems helps by:
a. Suggesting potential item-user interactions
b. Reducing node size
c. Only ranking products
d. Preprocessing data
Answer: aaionlinecourse+2
What is an embedding-based link prediction method?
a. Node2vec
b. Decision tree
c. Support vector clustering
d. Linear regression
Answer: amilvus+1
What can cause prediction error in link prediction models?
a. Network structure and noise
b. Balanced label distribution
c. Only dense graphs
d. Hyperparameter optimization
Answer: aonlinelibrary.wiley+1
Which type of graph is used in the case of user-item recommendation?
a. Bipartite graph
b. Complete graph
c. Directed acyclic graph
d. Simple chain
Answer: amilvus+2
Hybrid link prediction approaches combine:
a. Heuristic, probabilistic, and learning-based techniques
b. Only supervised methods
c. Just structural metrics
d. Label propagation
Answer: aaionlinecourse+2
For binary link prediction, a typical label for a positive example is:
a. 1
b. -1
c. 2
d. 0.5
Answer: aaionlinecourse+1
Link prediction in scientific networks helps identify:
a. Potential collaborations or co-authorships
b. Paper keywords
c. Author’s h-index only
d. Review deadlines
Answer: aarxiv+2
The ROC curve for link prediction plots:
a. TPR vs FPR
b. Node labels vs degrees
c. Clustering coefficient vs degree
d. Node color vs label
Answer: anature+1
Reinforcement learning can be applied to link prediction in dynamic networks for:
a. Adaptive exploration of evolving links
b. Deterministic clustering
c. Only static graphs
d. Binary encoding output
Answer: asciencedirect+1
The “cold start” problem in link prediction refers to:
a. Lack of prior information about nodes
b. Low computation power
c. Lack of test data only
d. High density of graph
Answer: aarxiv+2
Link prediction in security contexts can help:
a. Discover possible attack paths
b. Reduce encryption
c. Determine only node colors
d. Complete subnet masks
Answer: aexamradar+1
Node2vec node embeddings capture:
a. Both local and global graph structures
b. Only shared neighbors
c. Only edge weights
d. Fixed random projections
Answer: amilvus+1
Evaluation metrics for link prediction include all EXCEPT:
a. Silhouette coefficient
b. Precision-recall
c. AUC
d. F1-score
Answer: anature+1
Link prediction accuracy can be increased by:
a. Combining topological and feature-based approaches
b. Ignoring network attributes
c. Using only random walks always
d. Removing node attributes
Answer: awikipedia+2

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