Predicting Social Links for New Users across Aligned Heterogeneous Social Networks

Online social networks have gained great success in recent years and many of them involve multiple kinds of nodes and complex relationships. Among these relationships, social links among users are of great importance. Many existing link prediction methods focus on predicting social links that will appear in the future among all users based upon a snapshot of the social network. In real-world social networks, many new users are joining in the service every day. Predicting links for new users are more important. Different from conventional link prediction problems, link prediction for new users are more challenging due to the following reasons: (1) differences in information distributions between new users and the existing active users (i.e., old users); (2) lack of information from the new users in the network. We propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the link prediction problem for new users with information transferred from both the existing active users in the target network and other source networks through aligned accounts. We proposed a within-target-network personalized sampling method to process the existing active users' information in order to accommodate the differences in information distributions before the intra-network knowledge transfer. SCAN-PS can also exploit information in other source networks, where the user accounts are aligned with the target network. In this way, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network.Comment: 11 pages, 10 figures, 4 table

Detecting Strong Ties Using Network Motifs

Detecting strong ties among users in social and information networks is a fundamental operation that can improve performance on a multitude of personalization and ranking tasks. Strong-tie edges are often readily obtained from the social network as users often participate in multiple overlapping networks via features such as following and messaging. These networks may vary greatly in size, density and the information they carry. This setting leads to a natural strong tie detection task: given a small set of labeled strong tie edges, how well can one detect unlabeled strong ties in the remainder of the network? This task becomes particularly daunting for the Twitter network due to scant availability of pairwise relationship attribute data, and sparsity of strong tie networks such as phone contacts. Given these challenges, a natural approach is to instead use structural network features for the task, produced by {\em combining} the strong and "weak" edges. In this work, we demonstrate via experiments on Twitter data that using only such structural network features is sufficient for detecting strong ties with high precision. These structural network features are obtained from the presence and frequency of small network motifs on combined strong and weak ties. We observe that using motifs larger than triads alleviate sparsity problems that arise for smaller motifs, both due to increased combinatorial possibilities as well as benefiting strongly from searching beyond the ego network. Empirically, we observe that not all motifs are equally useful, and need to be carefully constructed from the combined edges in order to be effective for strong tie detection. Finally, we reinforce our experimental findings with providing theoretical justification that suggests why incorporating these larger sized motifs as features could lead to increased performance in planted graph models.Comment: To appear in Proceedings of WWW 2017 (Web-science track

Open and closed industry clusters: The social structure of innovation

In this paper we discuss knowledge and innovation in clusters and the benefits of clustering from a knowledge-based perspective. Knowledge-based resources and innovations are important sources of competitive advantage for firms. Aware of the importance of continuously seeking new knowledge firms increasingly seek knowledge-rich locations such as specific industry clusters across the world. These are locations characterized by the concentration of firms operating in related and supporting activities, a specialized work force and a specialized institutional environment that nurtures the industry. However, it is not likely that these clusters are always locations from which the firms will be able to draw the intended knowledge benefits. The social structure of the relationships between individuals and firms determines the extent to which knowledge will be created, will flow between co-located firms and bounds the knowledge benefits the firms may capture. We finish with a discussion of the need of further examination of the network dynamics involved in an industry cluster to obtain a clearer identification of the actual positive externalities that may accrue to co-locating firms.Strategy; Industry clusters; Innovation

Understanding the Footprint of the RBV in International Business Studies: the Last Twenty Years of Research

International business (IB) research has evolved substantially over the past four decades incorporating new concerns and theoretical contributions. During the past two decades, the Resource-Based View (RBV) has gained the preference of many IB scholars and has gradually become one of the dominant theoretical perspectives for studying IB decisions and operations. The 1991 article “Firm resources and sustained competitive advantage” by Jay Barney is recognized as a fundamental contribution to the Resource-Based View (RBV). In this paper we assess the influence of the RBV, proxied by Jay Barney’s (1991) article, on IB research over the twenty years period, from 1991 to 2010. In this bibliometric study of the articles published in the leading journal for IB research – Journal of International Business Studies (JIBS) – we conduct citation and co-citation analyses, the networks of co-authorship, and delve into the analysis of the key research topics. Beyond understanding the extant research we also contribute to i identifying future research avenues.info:eu-repo/semantics/publishedVersio

Networks, Information & Social Capital

This paper is based on a draft formerly titled “Network Structure & Information Advantage.”This paper investigates how information flows enable social networks to constitute social capital. By analyzing the information content encoded in email communication in an executive recruiting firm, we examine the long held but empirically untested assumption that diverse networks drive economic performance by providing access to novel information. We show that diverse networks provide diverse, novel information, and that access to novel information predicts productivity and performance. But whether diverse networks deliver novel information depends on a tradeoff between network diversity and communication channel bandwidth: as networks become diverse, channel bandwidth contracts. As network diversity and channel bandwidth both enable access to more novel information, diverse networks provide more novel information (a) when the topic space is large, (b) when topics are distributed non-uniformly across nodes and (c) when information in the network changes frequently. Diverse networks are not just pipes into diverse knowledge pools, but also inspire non-redundant communication even when the knowledge endowments of contacts are homogenous. Consistent with theories of cognitive capacity, bounded rationality, and information overload, there are diminishing marginal productivity returns to novel information. Network diversity also contributes to performance when controlling for the performance effects of novel information, suggesting additional non-information based benefits to structural diversity. These analyses unpack the mechanisms that enable information advantages in networks and serve as a 'proof-of-concept' for using email content to analyze relationships between information, networks and social capital in organizations.The National Science Foundation, Cisco Systems Inc., France Telecom and the MIT Center for Digital Business

Unveiling human interactions : approaches and techniques toward the discovery and representation of interactions in networks

L'abstract è presente nell'allegato / the abstract is in the attachmen