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Predicting Aggregate Social Activities using Continuous-Time Stochastic Process
"... How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness ..."
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How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness of social actors and become insufficient to deal with complex dynamics of user behaviors. In this paper, to address this issue, we first refine the notion of social activity to better describe dynamic user behaviors in social networks. We then propose a Parameterized Social Activity Model (PSAM) using continuous-time stochastic process for predicting aggregate social activities. With social activities evolving over time, PSAM itself also evolves and therefore dynamically captures the real-time characteristics of the current active population. Our experiments using two real social networks (Facebook and CiteSeer) reveal that the proposed PSAM model is effective in simulating social activity evolution and predicting aggregate social activities accurately at different time scales.
Classifying Trust/Distrust Relationships in Online Social Networks
"... Abstract—Online social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining li ..."
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Abstract—Online social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social bal-ance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slashdot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails. I.
Diffusion of “following” links in microblogging networks
- IEEE Transaction on Knowledge and Data Engineering (TKDE
, 2015
"... Abstract—When a “following ” link is formed in a social network, will the link trigger the formation of other neighboring links? We study the diffusion phenomenon of the formation of “following ” links by proposing a model to describe this link diffusion process. To estimate the diffusion strength b ..."
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Abstract—When a “following ” link is formed in a social network, will the link trigger the formation of other neighboring links? We study the diffusion phenomenon of the formation of “following ” links by proposing a model to describe this link diffusion process. To estimate the diffusion strength between different links, we first conduct an analysis on the diffusion effect in 24 triadic structures and find evident patterns that facilitate the effect. We then learn the diffusion strength in different triadic structures by maximizing an objective function based on the proposed model. The learned diffusion strength is evaluated through the task of link prediction and utilized to improve the applications of follower maximization and followee recommendation, which are specific instances of influence maximization. Our experimental results reveal that incorporating diffusion patterns can indeed lead to statistically significant improvements over the performance of several alternative methods, which demonstrates the effect of the discovered patterns and diffusion model. Index Terms—Link diffusion, Triad formation, Social network F
Time-aware Reciprocity Prediction in Trust Network
"... Abstract—Study of reciprocity helps to find influential factors for users building relationships, which greatly facilitates the social behavior understanding in trust networks. In the previous litera-ture, the dynamics of both network structure and user generated content are rarely considered. Our i ..."
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Abstract—Study of reciprocity helps to find influential factors for users building relationships, which greatly facilitates the social behavior understanding in trust networks. In the previous litera-ture, the dynamics of both network structure and user generated content are rarely considered. Our investigation of the available timing information from a real-world network demonstrates that time delay has significant impact on reciprocity formation. In particular, we find structural factors possess greater effect on short-term reciprocity while factors based on user generated content become more important for long-term reciprocity. Based on the empirical analysis, we redefine the reciprocity prediction problem as a learning task specific to each pair of users with different reciprocal delays. Evaluations show that our time-aware framework eventually outperforms the conventional classifiers that ignore the temporal information. Meanwhile, we tackle the problem of concept drift through fitting the evolving trend of features for Naive Bayes and performing periodic retraining for Logistic Regression classifiers, respectively.
CoupledLP: Link Prediction in Coupled Networks
"... We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as ..."
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We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any informa-tion of the target network. Moreover, the source and target net-works are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to lever-age the heterogeneous interactions between the two networks for the prediction task? We propose a unified framework, CoupledLP, to solve the prob-lem. Given two coupled networks, we first leverage atomic prop-agation rules to automatically construct implicit links in the target
CoupledLP: Link Prediction in Coupled Networks
"... We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as ..."
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We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any informa-tion of the target network. Moreover, the source and target net-works are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to lever-age the heterogeneous interactions between the two networks for the prediction task? We propose a unified framework, CoupledLP, to solve the prob-lem. Given two coupled networks, we first leverage atomic prop-agation rules to automatically construct implicit links in the target