Learning to predict reciprocity and triadic closure in social networks

Lou, Tiancheng; Tang, Jie; Hopcroft, John E.; Fang, Zhanpeng; Ding, Xiaowen

doi:10.1145/2499907.2499908

Cited by 91 publications

(74 citation statements)

References 31 publications

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“…While existing link prediction algorithms [40,16,3,41,56] are not designed to explain the network evolution in a dynamic setting, the MLE framework could in principle be used to assess which link prediction methods are more consistent with the longitudinal structural changes observed in the network, by treating the prediction at each step as a link creation strategy. These approaches will be explored in future work.…”

Section: Resultsmentioning

confidence: 99%

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The role of information diffusion in the evolution of social networks

Weng

Ratkiewicz

Perra

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

140

107

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Every day millions of users are connected through online social networks, generating a rich trove of data that allows us to study the mechanisms behind human interactions. Triadic closure has been treated as the major mechanism for creating social links: if Alice follows Bob and Bob follows Charlie, Alice will follow Charlie. Here we present an analysis of longitudinal micro-blogging data, revealing a more nuanced view of the strategies employed by users when expanding their social circles. While the network structure affects the spread of information among users, the network is in turn shaped by this communication activity. This suggests a link creation mechanism whereby Alice is more likely to follow Charlie after seeing many messages by Charlie. We characterize users with a set of parameters associated with different link creation strategies, estimated by a Maximum-Likelihood approach. Triadic closure does have a strong effect on link formation, but shortcuts based on traffic are another key factor in interpreting network evolution. However, individual strategies for following other users are highly heterogeneous. Link creation behaviors can be summarized by classifying users in different categories with distinct structural and behavioral characteristics. Users who are popular, active, and influential tend to create traffic-based shortcuts, making the information diffusion process more efficient in the network. Categories and Subject Descriptors KeywordsLink creation, traffic, network evolution, information diffusion, shortcut, user behavior, social media, network structure Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

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Section: Resultsmentioning

confidence: 99%

“…Common approaches consider link prediction as a classification task or ranking problem, using node similarity [40,32], the hierarchical structure of the network [16], random walks [3], graphical models [41], and user profile features [56].…”

Section: Introductionmentioning

confidence: 99%

The role of information diffusion in the evolution of social networks

Weng

Ratkiewicz

Perra

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

140

107

View full text Add to dashboard Cite

show abstract

“…The principle of homophily suggests that people tend to be connected with those who are similar to them [14]. It has been extensively studied and verified in online social networks [16,19] and mobile networks [4,12]. With the ego network of each user, we study the demographic homophily on both gender and age.…”

Section: Communication Demographicsmentioning

confidence: 99%

“…For SVM, we use liblinear 6 . For FGM, the model proposed in [19] is used. Note that our proposed DFG model is equal to FGM if we do not consider the interrelations between gender and age.…”

Section: Experiments Setupmentioning

confidence: 99%

Inferring user demographics and social strategies in mobile social networks

Dong

Yang

Tang

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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170

102

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Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns.Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people.We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable predictioninferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender/age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10% compared with several alternative methods.

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“…Romero and Kleinberg [16] studied the problem of triadic closure process and developed a methodology based on preferential attachment for studying the directed triadic closure process. Lou et al [11] investigated how a reciprocal link was developed from a parasocial relationship and how the relationships further developed into triadic closure on twitter dataset. There are also several works on social network analysis based on triadic closure.…”

Section: Introductionmentioning

confidence: 99%

Mining triadic closure patterns in social networks

Huang

Tang

et al. 2014

Proceedings of the 23rd International Conference on World Wide Web

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A closed triad is a group of three people who are connected with each other. It is the most basic unit for studying group phenomena in social networks. In this paper, we study how closed triads are formed in dynamic networks. More specifically, given three persons, what are the fundamental factors that trigger the formation of triadic closure? There are various factors that may influence the formation of a relationship between persons. Can we design a unified model to predict the formation of triadic closure? Employing a large microblogging network as the source in our study, we formally define the problem and conduct a systematic investigation. The study uncovers how user demographics and network topology influence the process of triadic closure. We also present a probabilistic graphical model to predict whether three persons will form a closed triad in dynamic networks. The experimental results on the microblogging data demonstrate the efficiency of our proposed model for the prediction of triadic closure formation.

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Learning to predict reciprocity and triadic closure in social networks

Cited by 91 publications

References 31 publications

The role of information diffusion in the evolution of social networks

The role of information diffusion in the evolution of social networks

Inferring user demographics and social strategies in mobile social networks

Mining triadic closure patterns in social networks

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