Modeling Relationship Strength for Link Prediction

Li, Ning; Xu, Feng; Ji, Shufan; Xu, Ke

doi:10.1007/978-3-642-39693-9_7

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…We include unweighted 19 and weighted 20 edge overlap, which we denote o ij and õij , respectively. Unweighted overlap is defined as in (1), and weighted overlap as in (2).…”

Section: Bow Tie Frameworkmentioning

confidence: 99%

“…Knowledge of the strength of a tie, as well as the social dynamics contributing to tie strength, has been shown to increase the accuracy of link prediction, enhance the modeling of the spread of disease and information, and lead to more targeted marketing. [2][3][4] Several indicators of tie strength have been proposed, perhaps most notably by Mark Granovetter in his seminal work The Strength of Weak Ties. 5 Granovetter differentiated between strong and weak ties and proposed the weak ties hypothesis: the stronger the tie between any two people, the higher the fraction of friends they have in common.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Social Bow Tie

Mattie¹,

Engø‐Monsen²,

Ling³

et al. 2017

Preprint

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Understanding tie strength in social networks, and the factors that influence it, have received much attention in a myriad of disciplines for decades. Several models incorporating indicators of tie strength have been proposed and used to quantify relationships in social networks, and a standard set of structural network metrics have been applied to predominantly online social media sites to predict tie strength. Here, we introduce the concept of the "social bow tie" framework, a small subgraph of the network that consists of a collection of nodes and ties that surround a tie of interest, forming a topological structure that resembles a bow tie. We also define several intuitive and interpretable metrics that quantify properties of the bow tie. We use random forests and regression models to predict categorical and continuous measures of tie strength from different properties of the bow tie, including nodal attributes. We also investigate what aspects of the bow tie are most predictive of tie strength in two distinct social networks: a collection of 75 rural villages in India and a nationwide call network of European mobile phone users. Our results indicate several of the bow tie

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“…We include unweighted 19 and weighted 20 edge overlap, which we denote o ij and õij , respectively. Unweighted overlap is defined as in (1), and weighted overlap as in (2).…”

Section: Bow Tie Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Social Bow Tie

Mattie¹,

Engø‐Monsen²,

Ling³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Xiang et al [21] formulate a latent variable model and propose a coordinate ascent optimization procedure to infer relationship strength in online social networks. Li et al [22] explore the important factors for users building their friendship and quantify the tie strength between friends. Leskovec et al [23] predict the sign of links as positive and negative in online social networks.…”

Section: Related Workmentioning

confidence: 99%

Time-aware reciprocity prediction in trust network

Xing¹,

Zhao²,

Fang³

et al. 2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

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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 literature, 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.

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“…Along with allowing an individual to optimally mobilize their social contacts [34,52], tie strength is used in various modern data-driven tasks such as predicting the formation of new ties [13,32,35], structuring online interactions [16], and defining the various social groups of an individual [39]. Moreover, the concept of tie strength is used throughout the social sciences, providing a valuable dimension to understand academic output [54], performance of teams [48], the formation of industry structures [57], social contagion [11], labor markets [40], political participation [56], and communication patterns [3].…”

Section: Introductionmentioning

confidence: 99%

“…Individuals may have their strongest ties with close friends and family, and their weakest ties with casual acquaintances, but they may also have ties of medium strength with individuals such as coworkers who they see regularly [38]. Moreover, in many settings, the only data available consists of which connections exist between individuals, without explicitly detailing the quality of the connections [21,38,32]. In these cases, the social network is defined by a set of individuals, referred to as nodes, and information about social connections between pairs of individuals, which are referred to as edges or ties.…”

Section: Introductionmentioning

confidence: 99%

Which Bridges Are Weak Ties? Algebraic Topological Insights on Network Structure and Tie Strength

Sarker¹,

Seby²,

Benson³

et al. 2021

Preprint

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A key question in the analysis of sociological processes on networks is to identify pairs of individuals who have strong or weak social ties. Existing approaches mathematically model the social network as a graph, and tie strength is often inferred by examining the number of shared neighbors between individuals, or equivalently, the number of triangles in the graph which contain a specific pair of individuals. However, this approach misses out on critical information because it does not distinguish the case where interactions occur among groups involving more than two individuals. In this work, we measure tie strength by explicitly accounting for these higher order interactions in the network, through the use of a new measure called Edge PageRank. We show how Edge PageRank can be interpreted as the steady-state outcome of a dynamic, message-passing social process that characterizes the strength of weak ties by appropriately discounting the effect of higher-order interactions involving three individuals. Empirically, we find that Edge PageRank outperforms standard measures in identifying tie strength in several large-scale social networks. These results provide a new perspective on tie strength and demonstrate the importance of incorporating higher-order interactions in social network analysis.

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Modeling Relationship Strength for Link Prediction

Cited by 10 publications

References 20 publications

The Social Bow Tie

The Social Bow Tie

Time-aware reciprocity prediction in trust network

Which Bridges Are Weak Ties? Algebraic Topological Insights on Network Structure and Tie Strength

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