Link prediction in complex networks: A survey
Reads0
Chats0
TLDR
Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.Abstract:
Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.read more
Citations
More filters
Journal ArticleDOI
Graph embedding techniques, applications, and performance: A survey
Palash Goyal,Emilio Ferrara +1 more
TL;DR: A comprehensive and structured analysis of various graph embedding techniques proposed in the literature, and the open-source Python library, named GEM (Graph Embedding Methods, available at https://github.com/palash1992/GEM ), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic.
Journal ArticleDOI
A Review of Relational Machine Learning for Knowledge Graphs
TL;DR: This paper provides a review of how statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph) and how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web.
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.
Proceedings Article
Network representation learning with rich text information
TL;DR: By proving that DeepWalk, a state-of-the-art network representation method, is actually equivalent to matrix factorization (MF), this work proposes text-associated DeepWalk (TADW), which incorporates text features of vertices into network representation learning under the framework of Matrix factorization.
Journal ArticleDOI
A Survey on Network Embedding
TL;DR: Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure as discussed by the authors, and a significant amount of progress has been made toward this emerging network analysis paradigm.
References
More filters
Journal ArticleDOI
Similarity index based on local paths for link prediction of complex networks.
TL;DR: A local path index to estimate the likelihood of the existence of a link between two nodes, and a network model with controllable density and noise strength in generating links, as well as collect data of six real networks.
Journal ArticleDOI
Efficient routing on complex networks.
TL;DR: A generalized routing algorithm is given to find the so-called efficient path, which considers the possible congestion in the nodes along actual paths, to improve the transportation efficiency on complex networks.
Journal ArticleDOI
Optimal Network Topologies for Local Search with Congestion
TL;DR: A formalism is presented that is able to cope simultaneously with the problem of search and the congestion effects that arise when parallel searches are performed, and expressions for the average search cost are obtained both in the presence and the absence of congestion.
Proceedings Article
Link Prediction in Relational Data
TL;DR: It is shown that the collective classification approach of RMNs, and the introduction of subgraph patterns over link labels, provide significant improvements in accuracy over flat classification, which attempts to predict each link in isolation.
Journal ArticleDOI
Link prediction based on local random walk
Weiping Liu,Linyuan Lü +1 more
TL;DR: This letter proposed a method based on local random walk, which can give competitively good or even better prediction than other random-walk–based methods while having a much lower computational complexity.