Social Network Analysis and Mining for Business Applications
TL;DR: The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.
Abstract: Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored.In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.
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TL;DR: How to use, and influence, consumer social communications to improve business performance, reputation, and profit.
Abstract: How to use, and influence, consumer social communications to improve business performance, reputation, and profit.
470 citations
TL;DR: An overview of online social networks is provided to contribute to a better understanding of this worldwide phenomenon and addresses the following questions: What are the major functionalities and characteristics ofOnline social networks?
270 citations
TL;DR: This survey aims to pave a comprehensive and solid starting ground for interested readers by soliciting the latest work in social influence analysis from different levels, such as its definition, properties, architecture, applications, and diffusion models.
197 citations
24 Aug 2014
TL;DR: WTFW ("Who to Follow and Why"), a stochastic topic model for link prediction over directed and nodes-attributed graphs, is proposed, which not only predicts links, but for each predicted link it decides whether it is a "topical" or a "social" link, and depending on this decision it produces a different type of explanation.
Abstract: User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty.In this paper we study link prediction with explanations for user recommendation in social networks. For this problem we propose WTFW ("Who to Follow and Why"), a stochastic topic model for link prediction over directed and nodes-attributed graphs. Our model not only predicts links, but for each predicted link it decides whether it is a "topical" or a "social" link, and depending on this decision it produces a different type of explanation.A topical link is recommended between a user interested in a topic and a user authoritative in that topic: the explanation in this case is a set of binary features describing the topic responsible of the link creation. A social link is recommended between users which share a large social neighborhood: in this case the explanation is the set of neighbors which are more likely to be responsible for the link creation.Our experimental assessment on real-world data confirms the accuracy of WTFW in the link prediction and the quality of the associated explanations.
171 citations
Cites background from "Social Network Analysis and Mining ..."
...Link prediction has been applied in a variety of domains, ranging from bioinformatics to web sites management, from bibliography to e-commerce [12, 18, 5]....
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References
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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
39,297 citations
"Social Network Analysis and Mining ..." refers background in this paper
..., the maximum possible distance between two nodes measured as length of the shortest path), exhibit smallworld structure [Watts and Strogatz 1998], and community structure [Girvan and Newman 2002], are only few of the ubiquitous properties that many researchers have verified....
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TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Abstract: Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
33,771 citations
Book•
25 Nov 1994TL;DR: This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of Dyadic and Triadic Interaction Models, which describes the relationships between actor and group measures and the structure of networks.
Abstract: Part I. Introduction: Networks, Relations, and Structure: 1. Relations and networks in the social and behavioral sciences 2. Social network data: collection and application Part II. Mathematical Representations of Social Networks: 3. Notation 4. Graphs and matrixes Part III. Structural and Locational Properties: 5. Centrality, prestige, and related actor and group measures 6. Structural balance, clusterability, and transitivity 7. Cohesive subgroups 8. Affiliations, co-memberships, and overlapping subgroups Part IV. Roles and Positions: 9. Structural equivalence 10. Blockmodels 11. Relational algebras 12. Network positions and roles Part V. Dyadic and Triadic Methods: 13. Dyads 14. Triads Part VI. Statistical Dyadic Interaction Models: 15. Statistical analysis of single relational networks 16. Stochastic blockmodels and goodness-of-fit indices Part VII. Epilogue: 17. Future directions.
17,104 citations
"Social Network Analysis and Mining ..." refers background in this paper
...INTRODUCTION Social network analysis emerged as an important research topic in sociology decades ago [Degene and Forse 1999; Scott 2000; Wasserman and Faust 1994; Freeman 2004], with the .rst studies focused on the adoption of medical and agricultural innovations [Coleman et al. 1966; Valente…...
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...Social network analysis emerged as an important research topic in sociology decades ago [Degene and Forse 1999; Scott 2000; Wasserman and Faust 1994; Freeman 2004], with the first studies focused on the adoption of medical and agricultural innovations [Coleman et al....
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TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Abstract: A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known—a collaboration network and a food web—and find that it detects significant and informative community divisions in both cases.
14,429 citations
"Social Network Analysis and Mining ..." refers background or methods in this paper
...…small diameter (i.e., the maximum possible distance between two nodes measured as length of the shortest path), exhibit smallworld structure [Watts and Strogatz 1998], and community structure [Girvan and Newman 2002], are only few of the ubiquitous properties that many researchers have veri.ed....
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...A different approach to hierarchical community detection was presented by Girvan and Newman [2002]. Instead of merging nodes in a bottom-up fashion, the method proceeds top down....
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..., the maximum possible distance between two nodes measured as length of the shortest path), exhibit smallworld structure [Watts and Strogatz 1998], and community structure [Girvan and Newman 2002], are only few of the ubiquitous properties that many researchers have verified....
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Proceedings Article•
11 Nov 1999
TL;DR: This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.
Abstract: The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.
14,400 citations