Link Prediction Based on Graph Neural Networks

link Prediction Based on Graph Neural NetworksMuhan ZhangDepartment of CSEW ashington University in St. ChenDepartment of CSEW ashington University in St. Prediction is a key problem for network -structured data. link predictionheuristics use some score functions, such as common neighbors and Katz index,to measure the likelihood of links. They have obtained wide practical uses due totheir simplicity, interpretability, and for some of them, scalability. However, everyheuristic has a strong assumption on when two nodes are likely to link , whichlimits their effectiveness on Networks where these assumptions fail. In this regard,a more reasonable way should be learning a suitable heuristic from a given networkinstead of using predefined ones. By extracting a local subgraph around each targetlink, we aim to learn a function mapping the subgraph patterns to link existence,thus automatically learning a heuristic that suits the current network .

a number of network embedding techniques have been proposed, such as DeepWalk [19], LINE [21] and node2vec [20], which are also latent feature methods since they implicitly factorize some matrices too [22]. Explicit features are often available in the form of node attributes, describing all kinds of side information about individual nodes.

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  Based, Network, Link, Prediction, Graph, Neural, Deepwalk, Link prediction based on graph neural networks

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