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Mining Structural Hole Spanners Through Information Diffusion in Social Networks
"... The theory of structural holes [4] suggests that individuals would benefit from filling the “holes ” (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information d ..."
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Cited by 24 (13 self)
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The theory of structural holes [4] suggests that individuals would benefit from filling the “holes ” (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information diffusion. However, there is still lack of a principled methodology to detect structural hole spanners from a given social network. In this work, we precisely define the problem of mining top-k structural hole spanners in large-scale social networks and provide an objective (quality) function to formalize the problem. Two instantiation models have been developed to implement the objective function. For the first model, we present an exact algorithm to solve it and prove its convergence. As for the second model, the optimization is proved to be NP-hard, and we design an efficient algorithm with provable approximation guarantees. We test the proposed models on three different networks: Coauthor, Twitter, and Inventor. Our study provides evidence for the theory of structural holes, e.g., 1 % of Twitter users who span structural holes control 25 % of the information diffusion on Twitter. We compare the proposed models with several alternative methods and the results show that our models clearly outperform the comparison methods. Our experiments also demonstrate that the detected structural hole spanners can help other social network applications, such as community kernel detection and link prediction. To the best of our knowledge, this is the first attempt to address the problem of mining structural hole spanners in large social networks.
Confluence: Conformity Influence in Large Social Networks
"... Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users ’ online behavior. We formally define se ..."
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Cited by 18 (8 self)
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Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users ’ online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10 % compared with several alternative methods.
Crosslingual knowledge linking across wiki knowledge bases
- In WWW’12, 459–468. ACM
, 2012
"... Wikipedia becomes one of the largest knowledge bases on the Web. It has attracted 513 million page views per day in January 2012. However, one critical issue for Wikipedia is that articles in different language are very unbalanced. For example, the number of articles on Wikipedia in English has reac ..."
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Cited by 16 (3 self)
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Wikipedia becomes one of the largest knowledge bases on the Web. It has attracted 513 million page views per day in January 2012. However, one critical issue for Wikipedia is that articles in different language are very unbalanced. For example, the number of articles on Wikipedia in English has reached 3.8 million, while the number of Chinese articles is still less than half million and there are only 217 thousand cross-lingual links between articles of the two languages. On the other hand, there are more than 3.9 million Chinese Wi-ki articles on Baidu Baike and Hudong.com, two popular encyclopedias in Chinese. One important question is how to link the knowledge entries distributed in different knowledge bases. This will immensely enrich the information in the on-line knowledge bases and benefit many applications. In this paper, we study the problem of cross-lingual knowledge link-ing and present a linkage factor graph model. Features are defined according to some interesting observations. Exper-iments on the Wikipedia data set show that our approach can achieve a high precision of 85.8 % with a recall of 88.1%. The approach found 202,141 new cross-lingual links between English Wikipedia and Baidu Baike.
Inferring Anchor Links across Multiple Heterogeneous Social Networks
"... Online social networks can often be represented as heterogeneous information networks containing abundant information about: who, where, when and what. Nowadays, people are usually involved in multiple social networks simultaneously. The multiple accounts of the same user in different networks are m ..."
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Cited by 12 (4 self)
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Online social networks can often be represented as heterogeneous information networks containing abundant information about: who, where, when and what. Nowadays, people are usually involved in multiple social networks simultaneously. The multiple accounts of the same user in different networks are mostly isolated from each other without any connection between them. Discovering the correspondence of these accounts across multiple social networks is a crucial prerequisite for many interesting inter-network applications, such as link recommendation and community analysis using information from multiple networks. In this paper, we study the problem of anchor link prediction across multiple heterogeneous social networks, i.e., discovering the correspondence among different accounts of the same user. Unlike most prior work on link prediction and network alignment, we assume that the anchor links are one-to-one relationships (i.e., no two edges share a common endpoint) between the accounts in two social networks, and a small number of anchor links are known beforehand. We propose to extract heterogeneous features from multiple heterogeneous networks for anchor link prediction, including user’s social, spatial, temporal and text information. Then we formulate the inference problem for anchor links as a stable matching problem between the two sets of user accounts in two different networks. An effective solution, Mna (Multi-Network Anchoring), is derived to infer anchor links w.r.t. the one-to-one constraint. Extensive experiments on two real-world heterogeneous social networks show that our Mna model consistently outperform other commonly-used baselines on anchor link prediction.
Link Prediction and Recommendation across Heterogeneous Social Networks
"... Abstract—Link prediction and recommendation is a fundamental problem in social network analysis. The key challenge of link prediction comes from the sparsity of networks due to the strong disproportion of links that they have potential to form to links that do form. Most previous work tries to solve ..."
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Cited by 11 (3 self)
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Abstract—Link prediction and recommendation is a fundamental problem in social network analysis. The key challenge of link prediction comes from the sparsity of networks due to the strong disproportion of links that they have potential to form to links that do form. Most previous work tries to solve the problem in single network, few research focus on capturing the general principles of link formation across heterogeneous networks. In this work, we give a formal definition of link recommendation across heterogeneous networks. Then we propose a ranking factor graph model (RFG) for predicting links in social networks, which effectively improves the predictive performance. Motivated by the intuition that people make friends in different networks with similar principles, we find several social patterns that are general across heterogeneous networks. With the general social patterns, we develop a transfer-based RFG model that combines them with network structure information. This model provides us insight into fundamental principles that drive the link formation and network evolution. Finally, we verify the predictive performance of the presented transfer model on 12 pairs of transfer cases. Our experimental results demonstrate that the transfer of general social patterns indeed help the prediction of links. Keywords-Social network analysis, Link prediction, Recommendation, Factor graph, Heterogeneous networks
Transferring Heterogeneous Links across Location-Based Social Networks
"... Location-based social networks (LBSNs) are one kind of online social networks offering geographic services and have been attracting much attention in recent years. LBSNs usually have complex structures, involving heterogeneous nodes and links. Many recommendation services in LBSNs (e.g., friend and ..."
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Cited by 8 (4 self)
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Location-based social networks (LBSNs) are one kind of online social networks offering geographic services and have been attracting much attention in recent years. LBSNs usually have complex structures, involving heterogeneous nodes and links. Many recommendation services in LBSNs (e.g., friend and location recommendation) can be cast as link prediction problems (e.g., social link and location link prediction). Traditional link prediction researches on LBSNs mostly focus on predicting either social links or location links, assuming the prediction tasks of different types of links to be independent. However, in many real-world LBSNs, the prediction tasks for social links and location links are strongly correlated and mutually influential. Another key challenge in link prediction on LBSNs is the data sparsity problem (i.e., “new network ” problem), which can be encountered when LBSNs branch into new geographic areas or social groups. Actually, nowadays, many users are involved in multiple networks simultaneously and users who just join one LBSN may have been using other LBSNs for a long time. In this paper, we study the problem of predicting multiple types of links simultaneously for a new LBSN across partially aligned LBSNs and propose a novel method TRAIL (TRAnsfer heterogeneous lInks across LBSNs). TRAIL can accumulate information for locations from online posts and extract heterogeneous features for both social links and location links. TRAIL can predict multiple types of links simultaneously. In addition, TRAIL can transfer information from other aligned networks to the new network to solve the problem of lacking information. Extensive experiments conducted on two real-world aligned LBSNs show that TRAIL can achieve very good performance and substantially outperform the baseline methods.
Patent Partner Recommendation in Enterprise Social Networks
"... It is often challenging to incorporate users ’ interactions into a recommendation framework in an online model. In this paper, we propose a novel interactive learning framework to formulate the problem of recommending patent partners into a factor graph model. The framework involves three phases: 1) ..."
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Cited by 6 (1 self)
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It is often challenging to incorporate users ’ interactions into a recommendation framework in an online model. In this paper, we propose a novel interactive learning framework to formulate the problem of recommending patent partners into a factor graph model. The framework involves three phases: 1) candidate generation, where we identify the potential set of collaborators; 2) candidate refinement, where a factor graph model is used to adjust the candidate rankings; 3) interactive learning method to efficiently update the existing recommendation model based on inventors ’ feedback. We evaluate our proposed model on large enterprise patent networks. Experimental results demonstrate that the recommendation accuracy of the proposed model significantly outperformsseveralbaselinesmethodsusingcontentsimilarity, collaborative filtering and SVM-Rank. We also demonstratetheeffectivenessandefficiencyoftheinteractivelearning, which performs almost as well as offline re-training, but with only 1 percent of the running time.
Meta-path based Multi-Network Collective Link Prediction
"... Online social networks o↵ering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simulta-neously to enjoy specific services provided by di↵erent net-works. Formally, social networks that share some common user ..."
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Cited by 6 (3 self)
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Online social networks o↵ering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simulta-neously to enjoy specific services provided by di↵erent net-works. Formally, social networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of social links in multiple partially aligned social networks at the same time, which is formally defined as the multi-network link (formation) pre-diction problem. In multiple partially aligned social net-works, users can be extensively correlated with each other by various connections. To categorize these diverse connec-tions among users, 7 “intra-network social meta paths ” and 4 categories of “inter-network social meta paths ” are pro-
Mining social media with social theories: A survey. SIGKDD Explorations
, 2014
"... The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social me-dia data is big, linked, noisy, highly unstructured and in-complete, and differs from data in traditional data mining, which cult ..."
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Cited by 6 (3 self)
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The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social me-dia data is big, linked, noisy, highly unstructured and in-complete, and differs from data in traditional data mining, which cultivates a new research field- social media mining. Social theories from social sciences are helpful to explain so-cial phenomena. The scale and properties of social media data are very different from these of data social sciences use to develop social theories. As a new type of social data, social media data has a fundamental question- can we ap-ply social theories to social media data? Recent advances in computer science provide necessary computational tools and techniques for us to verify social theories on large-scale so-cial media data. Social theories have been applied to mining social media. In this article, we review some key social theo-ries in mining social media, their verification approaches, in-teresting findings, and state-of-the-art algorithms. We also discuss some future directions in this active area of mining social media with social theories. 1.
Unsupervised link prediction using aggregative statistics on heterogeneous social networks
- In KDD ’13
, 2013
"... ABSTRACT The concern of privacy has become an important issue for online social networks. In services such as Foursquare.com, whether a person likes an article is considered private and therefore not disclosed; only the aggregative statistics of articles (i.e., how many people like this article) is ..."
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Cited by 5 (1 self)
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ABSTRACT The concern of privacy has become an important issue for online social networks. In services such as Foursquare.com, whether a person likes an article is considered private and therefore not disclosed; only the aggregative statistics of articles (i.e., how many people like this article) is revealed. This paper tries to answer a question: can we predict the opinion holder in a heterogeneous social network without any labeled data? This question can be generalized to a link prediction with aggregative statistics problem. This paper devises a novel unsupervised framework to solve this problem, including two main components: (1) a three-layer factor graph model and three types of potential functions; (2) a ranked-margin learning and inference algorithm. Finally, we evaluate our method on four diverse prediction scenarios using four datasets: preference (Foursquare), repost (Twitter), response (Plurk), and citation (DBLP). We further exploit nine unsupervised models to solve this problem as baselines. Our approach not only wins out in all scenarios, but on the average achieves 9.90% AUC and 12.59% NDCG improvement over the best competitors. The resources are available at
