Xun Liang

Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks

The last few years have witnessed the emergence and evolution of a vibrant research stream on a large variety of online social media network (SMN) platforms. Recognizing anonymous, yet identical users among multiple SMNs is still an intractable problem. Clearly, cross-platform exploration may help solve many problems in social computing in both theory and applications. Since public profiles can be duplicated and easily impersonated by users with different purposes, most current user identification resolutions, which mainly focus on text mining of users’ public profiles, are fragile. Some studies have attempted to match users based on the location and timing of user content as well as writing style. However, the locations are sparse in the majority of SMNs, and writing style is difficult to discern from the short sentences of leading SMNs such as Sina Microblog and Twitter. Moreover, since online SMNs are quite symmetric, existing user identification schemes based on network structure are not effective. The real-world friend cycle is highly individual and virtually no two users share a congruent friend cycle. Therefore, it is more accurate to use a friendship structure to analyze cross-platform SMNs. Since identical users tend to set up partial similar friendship structures in different SMNs, we proposed the Friend Relationship-Based User Identification (FRUI) algorithm. FRUI calculates a match degree for all candidate User Matched Pairs (UMPs), and only UMPs with top ranks are considered as identical users. We also developed two propositions to improve the efficiency of the algorithm. Results of extensive experiments demonstrate that FRUI performs much better than current network structure-based algorithms.

Structure Based User Identification across Social Networks

Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.

A Novel Edge Weighting Method to Enhance Network Community Detection

Community detection is one of the most popular issues in analyzing and understanding the networks. Existing works show that community detection can be enhanced by proper assignments of weights onto the edges of a network. Large numbers of edge weighting schemes have been developed to cope with this problem. However, hardly has a satisfied balance between the local and global weightings been found. In this paper, the problem of the local and global weighting balance is first proposed and discussed. The SimRank is next introduced as a novel edge weighting method. Furthermore, the fast Newman algorithm is extended to be applicable for a weighted network. Combined with the edge weighting techniques, the extended algorithm enhances the performance of the original algorithm significantly through exhaustive experiments. And by comparing with several weighting methods, the experiments demonstrate that the proposed algorithm is superior and more robust for different kinds of networks.

Fast-Solving Quasi-Optimal LS-S 3 VM Based on an Extended Candidate Set

The semisupervised least squares support vector machine (LS-S3VM) is an important enhancement of least squares support vector machines in semisupervised learning. Given that most data collected from the real world are without labels, semisupervised approaches are more applicable than standard supervised approaches. Although a few training methods for LS-S3VM exist, the problem of deriving the optimal decision hyperplane efficiently and effectually has not been solved. In this paper, a fully weighted model of LS-S3VM is proposed, and a simple integer programming (IP) model is introduced through an equivalent transformation to solve the model. Based on the distances between the unlabeled data and the decision hyperplane, a new indicator is designed to represent the possibility that the label of an unlabeled datum should be reversed in each iteration during training. Using the indicator, we construct an extended candidate set consisting of the indices of unlabeled data with high possibilities, which integrates more information from unlabeled data. Our algorithm is degenerated into a special scenario of the previous algorithm when the extended candidate set is reduced into a set with only one element. Two strategies are utilized to determine the descent directions based on the extended candidate set. Furthermore, we developed a novel method for locating a good starting point based on the properties of the equivalent IP model. Combined with the extended candidate set and the carefully computed starting point, a fast algorithm to solve LS-S3VM quasi-optimally is proposed. The choice of quasi-optimal solutions results in low computational cost and avoidance of overfitting. Experiments show that our algorithm equipped with the two designed strategies is more effective than other algorithms in at least one of the following three aspects: 1) computational complexity; 2) generalization ability; and 3) flexibility. However, our algorithm and other algorithms have similar levels of performance in the remaining aspects.

Exploration of polygons in online social networks

Online social networks have continued to attract increased attention since the introduction of this concept nearly three decades ago. Consequently, a study about the workings of online social networks may help in understanding the structure of human society and the characteristics of generic complex networks. Over the past few years, interest in neighboring nodes, which are the number of nodes between any two nodes, the number of neighbors and how many triangles are present in a social network have produced the concepts of a six-degree separation (Milgram, Psychol Today 2(1):60–67, 1967; Backstrom et al., Proceedings of the 4th annual ACM web science conference, p 33–42, 2012), a heavy tail in the degree distribution (Barabási, Albert, Science 286(5439):509–512, 1999) and a high clustering coefficient (Luce, Perry, Psychometrika 14(2):95–116, 1949; Watts, Strogatz, Nature 393(6684):440–442, 1998; Amaral et al., Proc Natl Acad Sci USA 97(21):11149–11152, 2000). In a similar manner, researchers have also been curious about how many polygons are present in a given online social network. Although much effort has been expended (Dantzig et al., International symposium on theory of graphs, p 77–83, 1967; Kamae, IEEE Trans Circuit Theory 14(2):166–171, 1967; Gotlieb, Corneil, Commun ACM 10(12):780–783, 1967; Welch, J ACM 13(2):205–210, 1966; Tiernan, Commun ACM 13(12):722–726, 1970; Tarjan, SIAM J Comput 2(3):211–216, 1973; Johnson, SIAM J Comput 4:77–84, 1975; Mateti, Deo, SIAM J Comput 5(5):90–99, 1976; Marinari et al., Europhys Lett 73(3):301–307, 2005), studying this subject, the inability to enumerate polygons has stymied an in depth understanding of the properties of polygons in an online social network. In the study described in this paper, the estimated number of polygons in an online social network is revealed. It was found that in the current widely used online social networks, e.g., Facebook, Twitter, the number of polygons increases drastically when the length of a polygon is below a set value and then it decreases rapidly. The average length of the network polygons was calculated and it was found that online social networks contain a relatively large average length of polygons. Based on this perspective, a massive labyrinth of polygons would make the online social networks appear to be very complicated. To further investigate this area, a generalized clustering coefficient was explored. Results showed that the generalized clustering coefficient appeared to descend exponentially with the length of polygon and expeditiously approached zero. This result suggested that the polygons with large lengths should be ignored in many scenarios. Since the polygons with lengths greater than five appeared to have little impact on the network, the online social networks appeared to be less complex than anticipated. The polygon is one of the fundamental problems in graph theory and complex networks, so that the work reported here may be beneficial for many disciplines, including transportation [7], engineering [8], computer science [9–16], physics (Birmelé et al., Proceedings of the 24th annual ACM–SIAM symposium on discrete algorithms, p 1884–1896, 2013), sociology (Motter, Albert, Phys Today 65:43, 2012), epidemiology (Feld, Am J Sociol 96(6):1464–1477, 1991; Cohen et al., Phys Rev Lett 91(24):12343, 2002), psychology (Sun et al., Sci Rep 4(6188):5099–5099, 2014), biology (Feiler, Kleinbaum, Psychol Sci, 2015), medicine (Kincaid, Pilette, Comput Appl Biosci Cabios 8:267–273, 1992), geography (Kim et al., Lancet 386(9989):145–153, 2015), etc.

An unsupervised user identification algorithm using network embedding and scalable nearest neighbour

Most of the current studies on social network (SN) mainly focused on a single SN platform. Integration of SNs can provide more sufficient user behaviour data and more complete network structure, and thus is rewarding to an ocean of studies on social computing. Recognizing the identical users across SNs, or user identification, naturally bridges the SNs through users and has attracted extensive attentions. Due to the fragmentation, inconsistency and disruption of the accessible information among SNs, user identification is still an intractable problem. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions are supervised or semi-supervised and require some given identified users or seed users. In the scenarios where seed users are hard to obtain, it is laborious to label the seed users manually. In this study, we proposed an unsupervised scheme by employing the reliability and consistence of friend relationships in different SNs, termed Unsupervised Friend Relationship-based User Identification algorithm (UFRUI). The UFRUI first models the network structure and embeds the feature of each user into a vector using network embedding technique, and then converts the user identification problem into a nearest neighbour problem. Finally, the matching user is computed using the scalable nearest neighbour algorithm. Results of experiments demonstrated that UFRUI performs much better than current state-of-art network structure-based algorithm without seed users.

Learning distributed representations for large-scale dynamic social networks

Learning distributed representations of symbolic data were introduced by Hinton[1], and first developed in modeling networks for learning the node vectors by Perozzi et al (2014). In this work, we proposed Dnps, a novel nodes embedding approach for acquiring distributed representations of large-scale dynamic social networks. Dnps is suitable for many types of social networks: dynamic/static, directed/undirected, and weighted/unweighted. Recently, several works of nodes embedding were proposed. However, they were designed for static networks, such as language networks. To address this problem, first, we develop a damping based positive sampling (DpS) algorithm to learn the hierarchical structure of social networks. Then, we devise a local search based DpS algorithm to obtain incremental information of network evolution. Finally, we show Dnpss potentials on future link prediction task for three real-life large-scale dynamic social networks. The results show that Dnps consistently outperforms all baseline methods and exhibits an improvement of 12%, 6%, 4% on Digg, Flickr and YouTube over the second-highest level, respectively. Moreover, Dnps is also scalable. For example, Dnps can speed up the training process in 2 ∼ 36 times compared with benchmarks on Flickr network. The source codes of the project is available online1.

A Succinct Distributive Big Data Clustering Algorithm Based on Local-Remote Coordination

Mining global patterns on big data distributed in many remote locations is a challenging task since transmitting big data in different remote data servers to the central server is prohibitively expensive. In this paper, we present a succinct distributive big data clustering algorithm based on local-remote coordination (DBDC-LRC) that aims to reduce the cost of big data transmission while maintaining an acceptable overall clustering accuracy. The algorithm is divided into three phases. In the first phase, the idea of Canopy algorithm is improved in the search for representative points with a clustering assumption that the decision boundary should lie in a low-density region, during which controllable thresholds are optimized. Noticing that in data mining a hyperellipsoid is more adaptable in shaping unknown data than a hypercube, we employ Mahalanobis distance as opposed to the Euclidean distance in determining the representative points in different remote data servers. Given that only a limited number of representative points, instead of all the remote data, are transmitted to the central server for clustering, the transmitting cost is reduced significantly. In the second phase, a weighted clustering method is used in mining the global patterns from the gathered information of representative points from various remote data servers. In the third phase, the mined global patterns are sent back to the original remote server and the related data are labeled with the same patterns according their representative points nearby. In this phase, Bayesian method is used to resolve the conflicts that one point is covered by several representative points in its neighborhood. Experiments show that DBDC-LRC is highly suitable for mining patterns from distributive big data, and the advantages of this approach include low cost, high accuracy, high robustness, and good expansibility.

Emergency Decision Support Architectures for Bus Hijacking Based on Massive Image Anomaly Detection in Social Networks

In bus hijacking, the availability of instant information in the scene may help the decision-making largely. In this paper, we discussed the significant value of the information acquisition in bus hijacking emergency from a qualitative analysis and quantitative description. Furthermore, we proposed an effective emergency decision support architecture for bus hijacking based on massive information in social networks. Last but not least, as to the core part of images discrimination, we build an image anomaly detection algorithm model. In the first step of the model, we conduct a Scale Invariant Feature Transform (SIFT) detection for images, and extract local feature descriptor; In the second step, the image feature vectors of the key points are subjected to further K-means clustering, so that we get the unified K-dimensional feature vectors; In the third step, we make the image classification with Support Vector Machine (SVM) classifier. This algorithm model achieves the image discrimination for bus hijacking emergency successfully, so that the information inside the bus could be transmitted to the outside effectively, and therefore provide a significant value for emergency decision-making.