Its experimental results show unprecedented performance, working consistently well on a wide range of problems. We compare well known similarity metrics and their suitability for link prediction in directed social networks. Second, based on the $\gamma$-decaying theory, we propose a new algorithm to learn heuristics from local subgraphs using a graph neural network (GNN). In this chapter we introduce link prediction methods and metrics for directed graphs. Our results show that local subgraphs reserve rich information related to link existence. The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs. First, we develop a novel $\gamma$-decaying heuristic theory. To perform inductive link prediction, our model aims to score the plausibility of a target triplet (u, r t, v), where r t is a target relation between a head node u and tail node v in a KG. In this paper, we study this heuristic learning paradigm for link prediction. By extracting a local subgraph around each target link, we aim to learn a function mapping the subgraph patterns to link existence, thus automatically learning a `heuristic' that suits the current network. HLGNN consists of three major components: line graph transformation, intra-type aggregation, and inter-layer aggregation. In this regard, a more reasonable way should be learning a suitable heuristic from a given network instead of using predefined ones. In this section, we describe the proposed heterogeneous line graph neural network (HLGNN), which is designed for link prediction in heterogeneous networks. An AMR-based Link Prediction Approach for Document-level Event Argument Extraction. However, every heuristic has a strong assumption on when two nodes are likely to link, which limits their effectiveness on networks where these assumptions fail. Yuqing Yang, Qipeng Guo, Xiangkun Hu, Yue Zhang, Xipeng Qiu, and Zheng Zhang. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. View a PDF of the paper titled Link Prediction Based on Graph Neural Networks, by Muhan Zhang and 1 other authors View PDF Abstract:Link prediction is a key problem for network-structured data.
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