Results 21 to 30 of about 7,799,039 (309)
Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs [PDF]
International Joint Conference on Artificial Intelligence, 2022 Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage. Most previous works learn entity-specific embeddings of entities, which cannot handle unseen entities.
Xiaohan Xu +4 more
semanticscholar +1 more source
Pairwise link prediction [PDF]
Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2019 Link prediction is a common problem in network science that transects many disciplines. The goal is to forecast the appearance of new links or to find links missing in the network. Typical methods for link prediction use the topology of the network to predict the most likely future or missing connections between a pair of nodes.
Nassar, Huda +2 more
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Deep Link-Prediction Based on the Local Structure of Bipartite Networks
Entropy, 2022 Link prediction based on bipartite networks can not only mine hidden relationships between different types of nodes, but also reveal the inherent law of network evolution. Existing bipartite network link prediction is mainly based on the global structure
Hehe Lv +3 more
doaj +1 more source
Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs [PDF]
AAAI Conference on Artificial Intelligence, 2021 Inductive link prediction---where entities during training and inference stages can be different---has been shown to be promising for completing continuously evolving knowledge graphs.
Jiajun Chen +3 more
semanticscholar +1 more source
Retweets as a Predictor of Relationships among Users on Social Media. [PDF]
PLoS ONE, 2017 Link prediction is the problem of detecting missing links or predicting future link formation in a network. Application of link prediction to social media, such as Twitter and Facebook, is useful both for developing novel services and for sociological ...
Sho Tsugawa, Kosuke Kito
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Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks
Information, 2021 The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the
Pokpong Songmuang +2 more
doaj +1 more source
Review on Learning and Extracting Graph Features for Link Prediction
Machine Learning and Knowledge Extraction, 2020 Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and ...
Ece C. Mutlu +3 more
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Taxonomy of Link Prediction for Social Network Analysis: A Review
IEEE Access, 2020 Link prediction is a technique to forecast future new or missing relationships between entities based on the current network information. Graph theory and network science are theoretical concepts that have influenced the link prediction research ...
Herman Yuliansyah +2 more
doaj +1 more source
Linkless Link Prediction via Relational Distillation [PDF]
International Conference on Machine Learning, 2022 Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood data dependency limits GNNs in practical deployments.
Zhichun Guo +6 more
semanticscholar +1 more source
Classification using link prediction [PDF]
Neurocomputing, 2019 Link prediction in a graph is the problem of detecting the missing links that would be formed in the near future. Using a graph representation of the data, we can convert the problem of classification to the problem of link prediction which aims at finding the missing links between the unlabeled data (unlabeled nodes) and their classes.
Seyed Amin Fadaee, Maryam Amir Haeri
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