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Network representation learning with ensemble methods
Neurocomputing, 2020Abstract This paper investigates network representation learning which involves network structures and labels. Most methods proposed so far try to utilize different kinds of network related data available in just one perfect model to learn the set of perfect embeddings, and then evaluate its performance comparing with other methods for downstream ...
Boyu Zhang, Ji Xiang, Xin Wang 0086
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Decoupled Representation Learning for Attributed Networks
IEEE Transactions on Knowledge and Data Engineering, 2021Network representation learning or network embedding has attracted wide attention due to its effectiveness on various network-oriented applications in recent years. Though large efforts have been made, they usually model the interactions between nodes reflected by network structure and attributes in a coupled way.
Hao Wang 0076 +5 more
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Representation Learning on Networks for Community Detection
2020 Eighth International Conference on Advanced Cloud and Big Data (CBD), 2020Community detection is a fundamental problem in network analysis. In recent years, network representation learning has been leveraged to help the detection of potential communities. However, representation learning and community detection in these studies are usually optimized independently.
Jingya Zhou +3 more
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Learning Representations in Directed Networks
2015We propose a probabilistic model for learning continuous vector representations of nodes in directed networks. These representations could be used as high quality features describing nodes in a graph and implicitly encoding global network structure. The usefulness of the representations is demonstrated on link prediction and graph visualization tasks ...
Oleg U. Ivanov, Sergey O. Bartunov
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A Review of Network Representation Learning
2019With the development of the technology, social software such as Facebook, Twitter, YouTube, QQ, WeChat has also achieved great development. According to the existing data, in the first quarter of 2018, WeChat’s monthly number has reached 1 billion [1].
Dan Xu +3 more
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Learning Network Representations with Neighboring Affinity
2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018This paper studies the problem of representing networks in a low-dimensional vector space, which is critical in network node classification, link prediction, and recommender systems. We observe that neighbors of a given node often contain rich contextual information which reveals the shared patterns that could be used to learn sparse network ...
Zhao Li 0007 +2 more
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Text-enhanced network representation learning
Frontiers of Computer Science, 2020Network representation learning called NRL for short aims at embedding various networks into low-dimensional continuous distributed vector spaces. Most existing representation learning methods focus on learning representations purely based on the network topology, i.e., the linkage relationships between network nodes, but the nodes in lots of networks ...
Yu Zhu +3 more
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Aligning Multiple PPI Networks with Representation Learning on Networks
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018Protein-protein interaction (PPI) network alignment has been motivating researches for the comprehension of the underlying crucial biological knowledge, such as conserved evolutionary pathways and functionally conserved proteins throughout different species. Existing PPI network alignment methods have tried to improve the coverage ratio by aligning all
Bo Song +4 more
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Representation of knowledge and learning on automata networks
1988We have seen that new techniques allow to design automata networks, capable of learning how to solve problems of high order. Those techniques clearly go beyond the limitations of the perceptron and are very different, in their spirit, from the techniques usually used in Artificial Intelligence.
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