Social networks now connect billions of people around the world, where individuals occupying different positions often represent different social roles and show different characteristics in their behaviors. The structural hole (SH) theory demonstrates that users occupying the bridging positions between different communities have advantages since they control the key information diffusion paths. Users of this type, known as SH spanners, are important when it comes to assimilating social network structures and user behaviors. In this article, we review the use of SHs theory in social network analysis, where SH spanners take advantage of both information and control benefits. We investigate the existing algorithms of SH spanner detection and classify them into information flow-based algorithms and network centrality-based algorithms. For practitioners, we further illustrate the applications of SH theory in various practical scenarios, including enterprise settings, information diffusion in social networks, software development, mobile applications, and machine learning (ML)-based social prediction. Our review provides a comprehensive discussion on the foundation, detection, and practical applications of SHs. The insights can facilitate researchers and service providers to better apply the theory and derive value-added tools with advanced ML techniques. To inspire follow-up research, we identify potential research trends in this area, especially on the dynamics of networks.

Dating apps have gained tremendous popularity during the past decade. Compared with traditional offline dating means, dating apps ease the process of partner finding significantly. While bringing convenience to hundreds of millions of users, dating apps are vulnerable to become targets of adversaries. In this article, we focus on malicious user detection in dating apps. Existing methods overlooked the signals hidden in the textual information of user interactions, particularly the interplay of temporal-spatial behaviors and textual information, leading to limited detection performance. To tackle this, we propose DatingSec, a novel malicious user detection system for dating apps. Concretely, DatingSec leverages long short-term memory neural networks (LSTM) and an attentive module to capture the interplay of users' temporal-spatial behaviors and user-generated textual content. We evaluate DatingSec on a real-world dataset collected from Momo, a widely used dating app with more than 180 million users. Experimental results show that DatingSec outperforms state-of-the-art methods and achieves an F1-score of 0.857 and AUC of 0.940.

Rumor spreading in online social networks can inflict serious damages on individual, organizational, and societal levels. This problem has been addressed via computational approach in recent years. The dominant computational technique for the identification of rumors is the binary classification that uses rumor and non-rumor for the training. In this method, the way of annotating training data points determines how each class is defined for the classifier. Unlike rumor samples that often are annotated similarly, non-rumors get their labels arbitrarily based on annotators' volition. Such an approach leads to unreliable classifiers that cannot distinguish rumor from non-rumor consistently. In this paper, we tackle this problem via a novel classification approach called one-class classification (OCC). In this approach, the classifier is trained with only rumors, which means that we do not need the non-rumor data points at all. For this study, we use two primary Twitter data sets in this field and extract 86 features from each tweet. We then apply seven one-class classifiers from three different paradigms and compare their performance. Our results show that this approach can recognize rumors with a high level of F1-score. This approach may influence the predominant mentality of scholars about computational rumor detection and puts forward a new research path toward dealing with this problem.

As a result of the blooming of online social networks (OSNs), a user often holds accounts on multiple sites. In this article, we study the emerging “cross-site linking” function available on mainstream OSN services including Foursquare, Quora, and Pinterest. We first conduct a data-driven analysis on crawled profiles and social connections of all 61.39 million Foursquare users to obtain a thorough understanding of this function. Our analysis has shown that the cross-site linking function is adopted by 57.10% of all Foursquare users, and the users who have enabled this function are more active than others. We also find that the enablement of cross-site linking might lead to privacy risks. Based on cross-site links between Foursquare and external OSN sites, we formulate cross-site information aggregation as a problem that uses cross-site links to stitch together site-local information fields for OSN users. Using large datasets collected from Foursquare, Facebook, and Twitter, we demonstrate the usefulness and the challenges of cross-site information aggregation. In addition to the measurements, we carry out a survey collecting detailed user feedback on cross-site linking. This survey studies why people choose to or not to enable cross-site linking, as well as the motivation and concerns of enabling this function.

Being a leading online service providing both local search and social networking functions, Foursquare has attracted tens of millions of users all over the world. Understanding the user behavior of Foursquare is helpful to gain insights for location-based social networks (LBSNs). Most of the existing studies focus on a biased subset of users, which cannot give a representative view of the global user base. Meanwhile, although the user-generated content (UGC) is very important to reflect user behavior, most of the existing UGC studies of Foursquare are based on the check-ins. There is a lack of a thorough study on tips, the primary type of UGC on Foursquare. In this article, by crawling and analyzing the global social graph and all published tips, we conduct the first comprehensive user behavior study of all 60+ million Foursquare users around the world. We have made the following three main contributions. First, we have found several unique and undiscovered features of the Foursquare social graph on a global scale, including a moderate level of reciprocity, a small average clustering coefficient, a giant strongly connected component, and a significant community structure. Besides the singletons, most of the Foursquare users are weakly connected with each other. Second, we undertake a thorough investigation according to all published tips on Foursquare. We start from counting the numbers of tips published by different users and then look into the tip contents from the perspectives of tip venues, temporal patterns, and sentiment. Our results provide an informative picture of the tip publishing patterns of Foursquare users. Last but not least, as a practical scenario to help third-party application providers, we propose a supervised machine learning-based approach to predict whether a user is an influential by referring to the profile and UGC, instead of relying on the social connectivity information. Our data-driven evaluation demonstrates that our approach can reach a good prediction performance with an F1-score of 0.87 and an AUC value of 0.88. Our findings provide a systematic view of the behavior of Foursquare users and are constructive for different relevant entities, including LBSN service providers, Internet service providers, and third-party application providers.

Networks are powerful tools for representing the relationships and interactions between entities in various disciplines. However, existing network analysis tools and packages either lack powerful functionality or are not scalable for large networks. In this descriptor, we present EasyGraph, an open-source network analysis library that supports several network data formats and powerful network mining algorithms. EasyGraph provides excellent operating efficiency through a hybrid Python/C++ implementation and multiprocessing optimization. It is applicable to various disciplines and can handle large-scale networks. We demonstrate the effectiveness and efficiency of EasyGraph by applying crucial metrics and algorithms to random and real-world networks in domains such as physics, chemistry, and biology. The results demonstrate that EasyGraph improves the network analysis efficiency for users and reduces the difficulty of conducting large-scale network analysis. Overall, it is a comprehensive and efficient open-source tool for interdisciplinary network analysis.