An Analysis of Virtual Nodes in Graph Neural Networks for Link Prediction (Extended Abstract)

EunJeong Hwang, Veronika Thost, Shib Sankar Dasgupta, Tengfei Ma

Keywords: virtual nodes, graph neural networks, link prediction

TL;DR: We propose new methods for extending graph neural networks with virtual nodes for link prediction and provide a detailed analysis.

Abstract: It is well known that the graph classification performance of graph neural networks often improves by adding an artificial virtual node to the graphs, which is connected to all graph nodes. Surprisingly, the advantage of using virtual nodes has never been theoretically investigated, and their impact on other problems is still an open research question. In this paper, we adapt the concept of virtual nodes to link prediction, where we usually have much larger, often very sparse or dense, and overall more heterogeneous graphs. In particular, we use multiple virtual nodes per graph and graph-based clustering to determine the connections to the graph nodes. We also provide a detailed theoretical analysis. We conducted experiments over different datasets of the Open Graph Benchmark, analyze the results in detail, and show that virtual nodes may yield rather stable performance increases and sometimes considerably boost performance.

Type Of Submission: Extended abstract (max 4 main pages).

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Type Of Submission: Extended abstract.

Software: https://github.com/eujhwang/vn-analysis