Sampling-based algorithm for link prediction in temporal networks

Ahmed, Nahla Mohamed; Chen, Ling; Wang, Yulong; Li, Bin; Li, Yun; Liu, Wei

doi:10.1016/j.ins.2016.09.029

Cited by 42 publications

(17 citation statements)

References 33 publications

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“…This idea uses probabilistic techniques to obtain similarity scores between nodes which has better results than other methods in standard data sets. Ahmed et al [12] have introduced a fast similarity-based method for predicting probabilistic links in time networks. In this method, first snapshots of network are connected to a weight diagram.…”

Section: Related Workmentioning

confidence: 99%

Improved Link Prediction in Social Networks using Label Propagation

2019

International Journal of Research Studies in Computer Science a

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Section: Related Workmentioning

confidence: 99%

Improved Link Prediction in Social Networks using Label Propagation

2019

International Journal of Research Studies in Computer Science a

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“…Liu et al [9] filter out the redundant links in the network to improve the accuracy of the k-shell method from the perspective of spreading dynamics. Ahmed et al [10] present an algorithm based on random walks in temporal networks. Path-based similarity metrics utilize more topological information of network for link prediction.…”

Section: Introductionmentioning

confidence: 99%

“…The attribute-based method mainly uses attributes to calculate the similarity between the pair of nodes and predicts whether the two nodes will establish the link based on the value. The more identical attributes two nodes contain, the more similar the two nodes are [10]. For example, in the citation network, Trouillon et al [12] predict mutual references between authors by using the information of the authors, such as schools, countries, and educational backgrounds.…”

Section: Introductionmentioning

confidence: 99%

The Deep Fusion of Topological Structure and Attribute Information for Link Prediction

et al. 2020

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The link prediction can be used to seek missing or future links in the network, so it has become a hot research topic. The network generally contains two types of information: the topological structure of network formed by the connection between nodes, and the attribute information of nodes. However, the existing topology-based link prediction algorithms consider little attribute information. In this paper, a novel algorithm called Network Embedding with Attribute Deep Fusion for Link Prediction (NEADF-LP) is proposed. We get the embedded vectors with topological structure and attribute information by structure encoder and attribute encoder respectively, and fuse two vectors deeply. Compared with mainstream baselines on CiteSeer and Cora datasets, the results show that the deep fusion of topological structure and attribute information improve the accuracy of link prediction effectively. INDEX TERMS Complex network, deep fusion, link prediction, network embedding.

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“…In addition to spatial features, there are also methods learn temporal information of dynamic networks. Some methods [8], [27]- [31] are designed to utilize a sequence of previous snapshots to predict future links, which integrates both structural information and temporal information to model the dynamic evolution process. Sina Sajadmanesh et al [27] introduced a Non-Parametric Generalized Linear Model(NP-GLM) to infer the potential probability distribution of the time based on the characteristics of the appear-ance time of links.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the dynamic feature of network, most recent snapshot is more reliable for the future link prediction. Nahla Mohamed Ahmed [8], [30], [31] proposed a damping coefficient to aggregate the global topology information of the snapshots at T moments(window sizes) so as to obtain better results. Wenchao Yu et al [28] proposed a link prediction model with spatial and temporal consistency (LIST) to predict links in a sequence of networks over time.…”

Section: Introductionmentioning

confidence: 99%

GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction

Chen,

Wang,

2018

Preprint

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Dynamic link prediction is a research hot in complex networks area, especially for its wide applications in biology, social network, economy and industry. Compared with static link prediction, dynamic one is much more difficult since network structure evolves over time. Currently most researches focus on static link prediction which cannot achieve expected performance in dynamic network. Aiming at low AUC, high Error Rate, add/remove link prediction difficulty, we propose GC-LSTM, a Graph Convolution Network (GC) embedded Long Short Term Memory network (LTSM), for end-to-end dynamic link prediction. To the best of our knowledge, it is the first time that GCN embedded LSTM is put forward for link prediction of dynamic networks. GCN in this new deep model is capable of node structure learning of network snapshot for each time slide, while LSTM is responsible for temporal feature learning for network snapshot. Besides, current dynamic link prediction method can only handle removed links, GC-LSTM can predict both added or removed link at the same time. Extensive experiments are carried out to testify its performance in aspects of prediction accuracy, Error Rate, add/remove link prediction and key link prediction. The results prove that GC-LSTM outperforms current state-of-art method.

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Sampling-based algorithm for link prediction in temporal networks

Cited by 42 publications

References 33 publications

Improved Link Prediction in Social Networks using Label Propagation

Improved Link Prediction in Social Networks using Label Propagation

The Deep Fusion of Topological Structure and Attribute Information for Link Prediction

GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction

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