MCQs

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Overall MCQs for Reference

Node classification refers to:
a. Classifying entire graphs as a single category
b. Assigning labels to nodes within graphs
c. Ranking the importance of nodes
d. Detecting communities in graphs
Answer: b. Assigning labels to nodes within graphssciencedirect+1
Which domain commonly uses node classification?
a. Image recognition
b. Speech-to-text
c. Social network analysis
d. Time series forecasting
Answer: c. Social network analysissciencedirect
Node classification is typically performed on:
a. Sequential data
b. Unstructured text
c. Graph-structured data
d. Binary trees
Answer: c. Graph-structured datastellargraph.readthedocs+1
In node classification, which is used to predict the label of an unlabeled node?
a. Only node’s own features
b. Global graph features
c. Aggregated features from neighboring nodes
d. Random assignment
Answer: c. Aggregated features from neighboring nodessciencedirect+1
What is the main advantage of graph neural networks (GNNs) in node classification?
a. Scalability to huge graphs only
b. Only works on tabular data
c. Ability to learn representations from graph structure and features
d. Requires manual feature engineering
Answer: c. Ability to learn representations from graph structure and featurespmc.ncbi.nlm.nih+2
Which of the following metrics is most commonly used to evaluate node classification performance?
a. Precision-recall curve
b. Macro-F1 score
c. RMSE
d. BLEU score
Answer: b. Macro-F1 scoresciencedirect
An example of a node classification task in biology is:
a. Protein function prediction in interaction networks
b. DNA sequencing
c. Weather prediction
d. Spam mail detection
Answer: a. Protein function prediction in interaction networkssciencedirect
What is “message passing” in the context of GNNs for node classification?
a. Passing messages in a chat system
b. Aggregating information from neighboring nodes in a graph
c. Email communication protocols
d. Propagation of errors in supervised learning
Answer: b. Aggregating information from neighboring nodes in a graphpmc.ncbi.nlm.nih+1
In node classification, labeled nodes in the training set are used to:
a. Test the classifier
b. Train the classifier
c. Prune the graph
d. Remove noise
Answer: b. Train the classifiersciencedirect
Which is a common use case for node classification in recommendation systems?
a. Predicting user-item links
b. Assigning categories to products or users
c. Detecting click fraud
d. Ranking search results
Answer: b. Assigning categories to products or userspmc.ncbi.nlm.nih+1
What does the “inductive” setting in node classification refer to?
a. Classifying only seen nodes
b. Generalizing to classify unseen nodes
c. Restricting to supervised setting
d. Using clustering algorithms
Answer: b. Generalizing to classify unseen nodessciencedirect
Which of the following is NOT a typical step in node classification?
a. Feature extraction
b. Label propagation
c. Regression analysis
d. Model evaluation
Answer: c. Regression analysissciencedirect
What is the purpose of a node embedding in node classification?
a. To convert node labels into adjacency matrices
b. To map nodes to low-dimensional vectors preserving structural properties
c. To generate random node features
d. To remove nodes from the graph
Answer: b. To map nodes to low-dimensional vectors preserving structural propertiessciencedirect+1
Semi-supervised learning in node classification means:
a. Only labeled data is used
b. Only unlabeled data is used
c. Both labeled and unlabeled nodes contribute to training
d. Self-supervised training with no labels
Answer: c. Both labeled and unlabeled nodes contribute to trainingpmc.ncbi.nlm.nih
A simple, interpretable algorithm for node classification is:
a. Graph Convolutional Network (GCN)
b. Random assignment
c. Decision Tree classifier
d. Word2vec
Answer: c. Decision Tree classifierscribd+1
The key property that node classification leverages in graphs is:
a. Independence of nodes
b. Spatial locality
c. Interconnectedness and relationships
d. Sequential patterns
Answer: c. Interconnectedness and relationshipssciencedirect
One application of node classification in cybersecurity is:
a. Detecting malware binaries
b. Classifying devices in a network map
c. Optimizing firewall rules
d. Compressing network traffic
Answer: b. Classifying devices in a network mapsciencedirect
Which model aggregates node features using mean or sum operations in message passing?
a. K-Means
b. CNN
c. GNN
d. RNN
Answer: c. GNNpmc.ncbi.nlm.nih+1
What is the primary function of the “classifier” in node classification pipelines?
a. Assigns predicted labels to nodes based on their features
b. Generates the input data
c. Normalizes graph edges
d. Encrypts node labels
Answer: a. Assigns predicted labels to nodes based on their featuresneo4j+1
“Label propagation” is best described as:
a. Passing the same label to all nodes
b. Utilizing the graph structure to spread labels from known to unknown nodes
c. Random assignment
d. Labeling based on degree centrality
Answer: b. Utilizing the graph structure to spread labels from known to unknown nodespmc.ncbi.nlm.nih+1
Which of these would NOT be a direct feature in node classification?
a. Node degree
b. Adjacency to labeled nodes
c. File size in bytes
d. Neighborhood label distribution
Answer: c. File size in bytessciencedirect
In a GNN, node representation is updated by:
a. Aggregating information from neighboring nodes
b. Only using the initial feature vector
c. Ignoring all neighbor nodes
d. Random sampling
Answer: a. Aggregating information from neighboring nodespmc.ncbi.nlm.nih+1
What is one reason for using random walks in node embedding methods?
a. To randomly remove nodes
b. To sample neighborhood structures
c. To quickly traverse the graph
d. For parallel computation only
Answer: b. To sample neighborhood structuressciencedirect+1
In node classification, what is often used to split data?
a. By time intervals
b. Training and test node sets
c. Only by random indices
d. Hierarchical clustering
Answer: b. Training and test node setssciencedirect
The micro-F1 metric is preferred for:
a. Overall classification accuracy across multiple classes
b. Regression quality
c. Edge prediction
d. Root mean squared error
Answer: a. Overall classification accuracy across multiple classessciencedirect
Deep learning advancements in node classification are primarily due to:
a. Better regularization methods
b. New activation functions
c. Efficient Graph Neural Network architectures
d. More labeled datasets only
Answer: c. Efficient Graph Neural Network architecturessciencedirect+1
Which node property is most relevant in message passing algorithms?
a. Node’s unique ID
b. Node’s degree
c. Node’s features and its neighbors’ features
d. Edge direction only
Answer: c. Node’s features and its neighbors’ featurespmc.ncbi.nlm.nih+1
The “homophily” assumption in node classification posits:
a. Dissimilar nodes are likely to be connected
b. Similar nodes are likely to share labels
c. Labels are randomly distributed
d. Labeled nodes have the highest degree
Answer: b. Similar nodes are likely to share labelssciencedirect
In transductive node classification:
a. The model is applied to train and test on the same graph
b. The model generalizes to unseen nodes
c. The model entirely ignores node features
d. There are no labeled nodes
Answer: a. The model is applied to train and test on the same graphsciencedirect
What is the role of “node property steps” in Neo4j’s node classification pipeline?
a. Remove duplicate nodes
b. Generate and configure features for classification
c. Compress graph size
d. Optimize query response
Answer: b. Generate and configure features for classificationneo4j
Node classification in recommendation systems helps to:
a. Predict future links exclusively
b. Assign user or item categories to improve recommendations
c. Detect fraudulent payments
d. Encrypt user data
Answer: b. Assign user or item categories to improve recommendationspmc.ncbi.nlm.nih+1
Fully connected layers in node embedding models are used for:
a. Creating adjacency matrices
b. Generating node features
c. Mapping node features into label probability vectors
d. Calculating graph diameter
Answer: c. Mapping node features into label probability vectorsarxiv+1
Label imbalance in node classification tasks causes:
a. Model underfitting always
b. Improved accuracy
c. Biased training towards majority label classes
d. Rapid overfitting of minority classes
Answer: c. Biased training towards majority label classessciencedirect
Edge weights in a graph can be used in node classification to:
a. Ignore all labels
b. Encode the strength of relationships in message aggregation
c. Remove noise from labels
d. Double the number of classes
Answer: b. Encode the strength of relationships in message aggregationsciencedirect
A popular node embedding algorithm, DeepWalk, uses:
a. Convolution over graphs
b. Random walks and skip-gram model
c. Only node degrees
d. Hierarchical clustering
Answer: b. Random walks and skip-gram modelsciencedirect
What does “graph convolution” enable in GCNs?
a. Aggregation of a node’s features and its neighbors’ features
b. Only convolution over image pixels
c. Sequential modeling
d. Encoding text features
Answer: a. Aggregation of a node’s features and its neighbors’ featuresstellargraph.readthedocs
Node classification can help in fraud detection by:
a. Classifying suspicious user or account nodes based on behavior and relations
b. Hiding labels from investigators
c. Removing all edges from the graph
d. Generating fake data
Answer: a. Classifying suspicious user or account nodes based on behavior and relationspmc.ncbi.nlm.nih+1
The most direct result of an effective node classification model is:
a. High label prediction accuracy for unlabeled nodes
b. Faster graph traversals
c. Larger graph file size
d. Shorter training times
Answer: a. High label prediction accuracy for unlabeled nodessciencedirect+1
“Node features” for classification may include:
a. Only one-hot encoded IDs
b. All available node attributes relevant to the task
c. Random noise vectors only
d. Only positional embeddings
Answer: b. All available node attributes relevant to the taskneo4j+1
In message passing algorithms, which operation is not typical for aggregating neighbor information?
a. Mean
b. Max
c. Concatenation
d. Multiplication by zero
Answer: d. Multiplication by zerosciencedirect
Node classification plays a key role in:
a. Predicting graph diameter
b. Molecular property prediction in chemistry
c. Operating system updates
d. Sorting files by size
Answer: b. Molecular property prediction in chemistrypmc.ncbi.nlm.nih
What is “attribute inference” in node classification?
a. Assigning missing features to nodes
b. Guessing the next edge to appear
c. Detecting unused nodes
d. Increasing node degree
Answer: a. Assigning missing features to nodessciencedirect
Feature normalization in node classification pipelines helps to:
a. Randomly shuffle features
b. Bring different features to a common scale for effective learning
c. Reduce the graph size
d. Compress the adjacency matrix
Answer: b. Bring different features to a common scale for effective learningneo4j
Multi-label node classification differs from single-label in that:
a. Each node can have multiple labels
b. Each node has no label
c. Labels are mutually exclusive
d. Graphs are not used
Answer: a. Each node can have multiple labelssciencedirect
Which evaluation metric would be least appropriate for a highly imbalanced node classification dataset?
a. Macro-F1 score
b. Micro-F1 score
c. Accuracy
d. Area under ROC curve
Answer: c. Accuracysciencedirect
Feature selection in node classification primarily aims to:
a. Reduce overfitting and improve interpretability
b. Add more random features
c. Increase model complexity
d. Duplicate node information
Answer: a. Reduce overfitting and improve interpretabilitysciencedirect
The output of node classification is generally:
a. Predicted classes or probabilities for each node
b. Full adjacency list of the graph
c. Only edge weights
d. Clustering of edges
Answer: a. Predicted classes or probabilities for each nodeneo4j+1
Batch normalization applied to node features in GNNs can:
a. Accelerate training and stabilize feature distribution
b. Randomize all labels
c. Increase overfitting
d. Remove graph structure
Answer: a. Accelerate training and stabilize feature distributionsciencedirect
Graph Attention Networks (GATs) in node classification use:
a. Weighted aggregation of neighbor features based on attention scores
b. Pure random walks with no weights
c. Only degree-based aggregation
d. Simple mean aggregation
Answer: a. Weighted aggregation of neighbor features based on attention scoressciencedirect
The final node classification deployment step involves:
a. Using the trained model to predict labels for previously unseen nodes
b. Deleting original labels
c. Building new graphs from scratch
d. Only visualizing results
Answer: a. Using the trained model to predict labels for previously unseen nodesneo4j+1

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