Open AccessProceedings Article
Beyond Link Prediction: Predicting Hyperlinks in Adjacency Space.
Muhan Zhang,Zhicheng Cui,Shali Jiang,Yixin Chen +3 more
- pp 4430-4437
Reads0
Chats0
TLDR
A new algorithm called Coordinated Matrix Minimization (CMM) is proposed, which alternately performs nonnegative matrix factorization and least square matching in the vertex adjacency space of the hypernetwork, in order to infer a subset of candidate hyperlinks that are most suitable to fill the training hypernetwork.Abstract:
This paper addresses the hyperlink prediction problem in hypernetworks. Different from the traditional link prediction problem where only pairwise relations are considered as links, our task here is to predict the linkage of multiple nodes, i.e., hyperlink. Each hyperlink is a set of an arbitrary number of nodes which together form a multiway relationship. Hyperlink prediction is challenging – since the cardinality of a hyperlink is variable, existing classifiers based on a fixed number of input features become infeasible. Heuristic methods, such as the common neighbors and Katz index, do not work for hyperlink prediction, since they are restricted to pairwise similarities. In this paper, we formally define the hyperlink prediction problem, and propose a new algorithm called Coordinated Matrix Minimization (CMM), which alternately performs nonnegative matrix factorization and least square matching in the vertex adjacency space of the hypernetwork, in order to infer a subset of candidate hyperlinks that are most suitable to fill the training hypernetwork. We evaluate CMM on two novel tasks: predicting recipes of Chinese food, and finding missing reactions of metabolic networks. Experimental results demonstrate the superior performance of our method over many seemingly promising baselines.read more
Citations
More filters
Proceedings Article
Link prediction based on graph neural networks
Muhan Zhang,Yixin Chen +1 more
TL;DR: A novel $\gamma$-decaying heuristic theory is developed that unifies a wide range of heuristics in a single framework, and proves that all these heuristic can be well approximated from local subgraphs.
Posted Content
Link Prediction Based on Graph Neural Networks
Muhan Zhang,Yixin Chen +1 more
TL;DR: Zhang et al. as discussed by the authors proposed to learn a function mapping the subgraph patterns to link existence by extracting a local subgraph around each target link, thus automatically learning a ''heuristic'' that suits the current network.
Posted Content
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
Naganand Yadati,Madhav Nimishakavi,Prateek Yadav,Vikram Nitin,Anand Louis,Partha Pratim Talukdar +5 more
TL;DR: HyperGCN as mentioned in this paper is a graph convolutional network (GCN) for hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabeled vertices in a hypergraph.
Proceedings Article
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
Naganand Yadati,Madhav Nimishakavi,Prateek Yadav,Vikram Nitin,Anand Louis,Partha Pratim Talukdar +5 more
TL;DR: This work proposes HyperGCN, a novel GCN for SSL on attributed hypergraphs, and shows how it can be used as a learning-based approach for combinatorial optimisation on NP-hard hypergraph problems.
Posted Content
Subgraph Neural Networks
TL;DR: A novel subgraph routing mechanism that propagates neural messages between the subgraph's components and randomly sampled anchor patches from the underlying graph, yielding highly accurate subgraph representations, as well as designing a series of new synthetic and real-world subgraph datasets.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Proceedings ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal IssueDOI
The link-prediction problem for social networks
David Liben-Nowell,Jon Kleinberg +1 more
TL;DR: Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
Journal ArticleDOI
A new status index derived from sociometric analysis.
TL;DR: A new method of computation which takes into account who chooses as well as how many choose is presented, which introduces the concept of attenuation in influence transmitted through intermediaries.