Graph Neural Networks (GNNs) address the challenge of applying machine learning to graph-structured data, which traditional models cannot handle directly. This is because traditional machine learning models cannot use the relationship information in the graph data, and also they work on data with regular grid like structures like images.
This paper surveys GNNs as encoder-decoder models, demonstrating their application across various graph tasks, and revealing their behavior through experiments.
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https://arxiv.org/abs/2412.19419
📌 Graph Neural Networks message passing creates a inductive bias. GNN favor solutions where nearby nodes have similar embeddings. This is effective for high-homophily graphs, unlike shallow embedding methods.
📌 Graph Neural Networks encoder's modular design (pre-processing, message-passing, post-processing) enables task-specific tuning. This is key. The paper finds tuning effective even with very limited labeled data (1% training).
📌 Graph Neural Networks balance expressiveness and scalability via different message-passing types. Convolutional approaches suit high-homophily cases. Attentional or concatenation-based methods offer more flexibility for complex, low-homophily scenarios.
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Methods Explored in this Paper 🔧:
→ GNNs use an encoder-decoder framework. The encoder maps nodes to low-dimensional embeddings.
→ The encoder includes optional pre-processing, message-passing, and optional post-processing layers. Message-passing layers aggregate information from node neighborhoods.
→ Three categories of message-passing layers exist: Convolutional, Message-Passing, and Attentional. Each differs in how node features interact. Convolutional use fixed weights, Message-Passing (MP) networks uses learned functions on node pairs and Attentional networks learn weights.
→ Decoders transform embeddings into task-specific predictions. Example loss functions are cross-entropy for node classification, and mean squared error for node regression.
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Key Insights 💡:
→ GNN performance varies with graph homophily (tendency of connected nodes to share the same label) and Signal-to-Noise Ratio (SNR) (strength of node features).
→ Tuning hyperparameters offers significant gains in medium-difficulty scenarios.
→ Message-passing layers reduce node feature noise in high-homophily graphs. Concatenation in GraphSAGE helps preserve node-specific information.
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Results 📊:
→ GraphSAGE outperforms GCN and GATv2 on low-homophily graphs by over 10% (56.69% vs 44.89% for GATv2 and 37.69% for GCN) in node classification accuracy with 80% training data.
→ GCN outperforms others on high homophily graph by over 1.5% (with 85.31% accuracy vs GATv2's 86.95 ) with 80% training data.
→ Tuned GNNs can outperform off-the-shelf RevGNNs (another GNN-based architecture) in node classification by 12% (62.32% vs 50.93%) on low homophily datasets with 80% training.
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