Relational learning via latent social dimensions

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13, 2013

Social media expands the ways people communicate with each other. On a popular social media website, a user typically has hundreds of contacts (or friends) on average. As a person's social network grows, friend management is increasingly important for effective communications. Often, one can only afford to maintain close friendship in a small scale due to limited time and other resources. In other words, the majority of one's connections are so-so friends and do not hold strong influence on the user. One approach resorts to network denoising, by which unimportant connections are removed as noise. We study the challenges of network denoising in social media and how we can leverage a variety of social media information to denoise the links. We formulate the network denoising task as an optimization problem, and show the efficacy of our network denoising approach and its scalability experimentally in the domain of behavior inference.

Community detection in online social networks is typically based on the analysis of the explicit connections between users, such as "friends" on Facebook and "followers" on Twitter. But online users often have hundreds or even thousands of such connections, and many of these connections do not correspond to real friendships or more generally to accounts that users interact with. We claim that community detection in online social networks should be question-oriented and rely on additional information beyond the simple structure of the network. The concept of 'community' is very general, and different questions such as "whom do we interact with?" and "with whom do we share similar interests?" can lead to the discovery of different social groups. In this paper we focus on three types of communities beyond structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that the communities obtained in the three weighted cases are highly different from each other, and from the communities obtained by considering only the unweighted structural network. Our results confirm that asking a precise question is an unavoidable first step in community detection in online social networks, and that different questions can lead to different insights about the network under study.

2011

Abstract Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations.

International journal of engineering research and technology, 2013

Day by day the clicks are increasing in a particular network. Behaviour is nothing but to know the interest and requirement of users. Collective behaviour, which indicates the group of data generated on a large scale. In this framework affiliations of actors are capture by extracting social dimensions and then classify the actors using extracted dimensions. As existing approaches to extract social dimensions are not scalable and can not handle network of huge size. We solve these problem by sparsifying social dimension to make this extraction scalable by using edge-centric clustering scheme and k-means variant algorithm. In social media, multiple modes of actors can be involved in the same network, resulting in a multimode network. In this work, we attempt to harness the predictive power of social connections to determine the preferences or behaviours of individuals such as whether a user supports a certain political view, whether one likes one product, whether he/she would like to vote for a presidential candidate, etc.

Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18

We consider the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as hometown, current city, and employers for people connected by a social network; by predicting these user profile fields, the network can provide a better experience to its users. Existing approaches such as Label Propagation (Zhu et al., 2003) fail to consider interactions between the label types. Our proposed method, called Edge-Explain, explicitly models these interactions, while still allowing scalable inference under a distributed message-passing architecture. On a large subset of the Facebook social network, collected in a previous study (Chakrabarti et al., 2014), EdgeExplain outperforms label propagation for several label types, with lifts of up to 120% for recall@1 and 60% for recall@3.

Many social network applications face the following problem: given a network G = (V, E) with labels on a small subset O ⊂ V of nodes and an optional set of features on nodes and edges, predict the labels of the remaining nodes. Much research has gone into designing learning models and inference algorithms for accurate predictions in this setting. However, a core hurdle to any prediction effort is that for many nodes there is insufficient evidence for inferring a label. We propose that instead of focusing on the impossible task of providing high accuracy over all nodes, we should focus on selectively making the few node predictions which will be correct with high probability. Any selective prediction strategy will require that the scores attached to node predictions be well-calibrated. Our evaluations show that existing prediction algorithms are poorly calibrated. We propose a new method of training a graphical model using a conditional likelihood objective that provides better calibration than the existing joint likelihood objective. We augment it with a decoupled confidence model created using a novel unbiased training process. Empirical evaluation on two large social networks show that we are able to select a large number of predictions with accuracy as high as 95%, even when the best overall accuracy is only 40%.

2007

Abstract Scale is often an issue when attempting to understand and analyze large social networks. As the size of the network increases, it is harder to make sense of the network, and it is computationally costly to manipulate and maintain. Here we investigate methods for pruning social networks by determining the most relevant relationships in a social network. We measure importance in terms of predictive accuracy on a set of target attributes of social network groups.

International Journal of Social Network Mining, 2012

One of the most appreciated functionality of computers nowadays is their being a means for communication and information sharing among people. With the spread of the internet, several complex interactions have taken place among people, giving rise to huge information networks based on these interactions. Social networks potentially represent an invaluable source of information that can be exploited for scientific and commercial purposes. On the other hand, due to their distinguishing peculiarities (huge size and inherent relational setting) with respect to all previous information extraction tasks faced in computer science, they require new techniques to gather this information. Social network mining (SNM) is the corresponding research area, aimed at extracting information about the network objects and behaviour that cannot be obtained based on the explicit/implicit description of the objects alone, ignoring their explicit/implicit relationships. Statistical relational learning (SRL) is a very promising approach to SNM, since it combines expressive representation formalisms, able to model complex relational networks, with statistical methods able to handle uncertainty about objects and relations. This paper is a survey of some SRL formalisms and techniques adopted to solve some SNM tasks.

2009

Abstract The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr and YouTube present opportunities and challenges to studying collective behavior in a large scale. In this work, we aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network?

Knowledge and Data Engineering, …, 2012

This study of collective behavior is to understand how individuals behave in a social networking environment. Oceans of data generated by social media like Facebook, Twitter, Flickr, and YouTube present opportunities and challenges to study collective behavior on a large scale. In this work, we aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? A social-dimension-based approach has been shown effective in addressing the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands of actors. The scale of these networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the proposed approach can efficiently handle networks of millions of actors while demonstrating a comparable prediction performance to other non-scalable methods.