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
A Fast Overlapping Community Detection Algorithm Based on Weak Cliques for Large-Scale Networks
TL;DR: A weak-CPM is proposed for overlapping community detection in large-scale networks and a new measure for characterizing the similarity between weak cliques is also suggested to check whether the weak clique can be merged into a community.
Journal ArticleDOI
Exploring dynamic research interest and academic influence for scientific collaborator recommendation
TL;DR: This paper proposes a most Beneficial Collaborator Recommendation model called BCR, which learns on researchers’ publications and associates three academic features: topic distribution of research interest, interest variation with time andResearchers’ impact in collaborators network.
Posted Content
A Survey of Adversarial Learning on Graphs
TL;DR: This work surveys and unify the existing works w.r.t. attack and defense in graph analysis tasks, and gives appropriate definitions and taxonomies at the same time, and emphasizes the importance of related evaluation metrics.
Journal ArticleDOI
A comprehensive survey of edge prediction in social networks: Techniques, parameters and challenges
TL;DR: A categorical review of the edge prediction methods in way to draw specific research problems to address further such as: complexity, accuracy, computational overhead and cost, scalability, generalization and performance issues.
Journal ArticleDOI
Link prediction in weighted social networks using learning automata
TL;DR: This paper tries to estimate the weight of each test link directly from the weights information in the network, taking advantage of using learning automata, intelligent tools that try to learn the optimal action based on reinforcement signals.
References
More filters
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.