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
Empirical analysis of web-based user-object bipartite networks
TL;DR: Wang et al. as mentioned in this paper investigated the correlation between degree and selection diversity and reported some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.
Book ChapterDOI
Using friendship ties and family circles for link prediction
TL;DR: It is shown that when there are tightly-knit family circles in a social network, the accuracy of link prediction models can be improved, by making use of the family circle features based on the likely structural equivalence of family members.
Journal ArticleDOI
Currency and commodity metabolites: their identification and relation to the modularity of metabolic networks
Mikael Huss,Petter Holme +1 more
TL;DR: It is argued that cross-modular edges are the key for the robustness of metabolism, found to be more modular than random networks but far from perfectly divisible into modules.
Journal ArticleDOI
Empirical analysis of web-based user-object bipartite networks
TL;DR: This letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, and proposes a new index, named collaborative similarity, to quantify the diversity of tastes based on the collaborative selection.