Link Prediction with Spatial and Temporal Consistency in Dynamic Networks

Yu, Wenchao; Cheng, Wei; Aggarwal, Charų C.; Chen, Haifeng; Wang, Wei

doi:10.24963/ijcai.2017/467

Cited by 65 publications

(39 citation statements)

References 18 publications

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“…We also discuss approaches for graphs with dynamic structure, since we can look on our transactional graph in that way. Authors of works [18,35] propose procedures based on the training on a single graph. These approaches cannot be straightforwardly generalized for the subgraphs, while the usage for the whole graph is prohibitive due to scalability issues.…”

Section: Related Workmentioning

confidence: 99%

Linking bank clients using graph neural networks powered by rich transactional data

Shumovskaia

Fedyanin

Sukharev

et al. 2021

Int J Data Sci Anal

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Financial institutions obtain enormous amounts of data about client transactions and money transfers, which can be considered as a large graph dynamically changing in time. In this work, we focus on the task of predicting new interactions in the network of bank clients and treat it as a link prediction problem. We propose a new graph neural network model, which uses not only the topological structure of the network but rich time-series data available for the graph nodes and edges. We evaluate the developed method using the data provided by a large European bank for several years. The proposed model outperforms the existing approaches, including other neural network models, with a significant gap in ROC AUC score on link prediction problem and also allows to improve the quality of credit scoring.

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Section: Related Workmentioning

confidence: 99%

Linking bank clients using graph neural networks powered by rich transactional data

Shumovskaia

Fedyanin

Sukharev

et al. 2021

Int J Data Sci Anal

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show abstract

“…Dynamic networks analysis [1] is widely concerned in many fields. Most of the current research about it mainly focus on community detection [2], [17], evolutionary analysis [18], [19], link prediction [13], [14], and anomaly detection [20], [21]. Here, we give a general overview on evolutionary analysis for dynamic network, which is the most related for this work.…”

Section: Related Workmentioning

confidence: 99%

“…These algorithms tend to significantly outperform the others. A few other methods were just unilaterally designed for either predicting community structure or links [14], [15] from a mission-oriented perspective.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Local and Global Evolution Pattern of Community Structures for Dynamic Networks Analysis

Wang

Jiao

et al. 2019

IEEE Access

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Exploring and understanding the temporal structure of dynamic networks attract extensive attention over the past few years. Most of these current research focuses on temporal community detection, evolution analysis or link prediction from a mission-oriented perspective. In fact, these three tasks should be not isolated but mutually reinforcing. Transforming these three tasks into a unified framework, it is crucial to extract the evolution pattern, which helps to understand the time-varying characteristics of temporal structure in essence. In addition, to the best of our knowledge, there is no work focusing on modeling and uncovering the local and global evolution pattern hidden in temporal community structure, simultaneously. In this paper, we propose a novel framework based on Orthogonal Nonnegative Matrix Factorization to Explore the Evolution Pattern (ONMF-EEP) for analyzing and predicting the time-varying structures in dynamic networks from local and global perspectives. The nature of this framework assumes that community structures are subject to a local evolution pattern (LEP) at each snapshot, and these LEPs are from a common global evolution pattern (GEP). The framework can synchronously detect temporal community structure, extract evolution pattern, and predict structure including communities and future snapshot links. The extensive experiments on real-world networks and artificial networks demonstrate that our proposed framework is highly effective on the tasks of dynamic network analysis.INDEX TERMS Orthogonal non-negative matrix factorization (ONMF), temporal community detection, evolutionary pattern extraction, structure prediction.

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“…Li et al [27] propose a time difference labeled based approach to capture the correlation between structural edges and time information in predicting the links and their building time. In addition, this work is also remotely related to the link prediction problem on dynamic networks [9,24,43,44].…”

Section: Link Prediction With Time Informationmentioning

confidence: 99%

Understanding and Predicting Delay in Reciprocal Relations

Tang

Wang

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Each reciprocity relation is formed by two parasocial links in a back-and-forth manner with a time delay. Hence, understanding the delay can help us gain better insights into the underlying mechanisms of network dynamics. Meanwhile, the accurate prediction of delay has practical implications in advancing a variety of real-world applications such as friend recommendation and marketing campaign. For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing. This paper presents the initial investigation of the time delay in reciprocal relations. Our study is based on a large-scale directed network from Tumblr that consists of 62.8 million users and 3.1 billion user following relations with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal a number of interesting patterns about the delay that motivate the development of a principled learning model to predict the delay in reciprocal relations. Experimental results on the above mentioned dynamic networks corroborate the effectiveness of the proposed delay prediction model.

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Link Prediction with Spatial and Temporal Consistency in Dynamic Networks

Cited by 65 publications

References 18 publications

Linking bank clients using graph neural networks powered by rich transactional data

Linking bank clients using graph neural networks powered by rich transactional data

Modeling the Local and Global Evolution Pattern of Community Structures for Dynamic Networks Analysis

Understanding and Predicting Delay in Reciprocal Relations

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