An introduction to the special issue on cross-community mining

These proceedings contain the papers selected for presentation at the seventh edition of the ACM SIGSPATIAL Workshop on Location-Based Social Networks (LBSN 2014) which is being held in conjunction with the ACM SIGSPATIAL GIS conference in 2014. Social networks have been prevalent on the Internet and become a hot research topic attracting many professionals from a variety of fields. The advances in location-acquisition and mobile communication technologies empower people to use location data with existing online social networks. The dimension of location helps bridge the gap between the physical world and online social networking services. Furthermore, people in an existing social network can expand their social structure with the new interdependency derived from their locations. As location is one of the most important components of user context, extensive knowledge about an individual's interests, behaviors, and relationships with others can be learned from locations. These kinds of location-embedded and location-driven social structures are known as location-based social networks. The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of LBSN development and applications, present their ideas and contributions, and set future directions in emerging innovative research for location based social networks. This year program is composed of two tracks of predominantly "social" and "spatial" views on LBSNs.

In the last two decades, the issue of Influence Maximization (IM) in traditional online social networks has been extensively studied since it was proposed. It is to find a seed set which has maximum influence spread under a specific network transmission model. However, in real life, the information can be spread not only through online social networks, but also between neighbors who are close to each other in the physical world. Location-Based Social Network (LBSN) is a new type of social network which is emerging increasingly nowadays. In a LBSN, users can not only make friends, but also share the events they participate in at different locations by checking in. In this paper, we aim to study the IM in LBSNs, where we consider both the influence of online and offline interactions. A two-layer network model and an information propagation model are proposed. Also, we formalize the IM problem in LBSNs and present an algorithm obtaining an approximation factor of \((1 - 1/e - \epsilon \)) in near-linear expected time. The experimental results show that the algorithm is efficient meanwhile offering strong theoretical guarantees.

The theme of the MDMKDD'13 workshop is\mining large scale rich content in a networked society." The workshop brought together experts in computational analysis on digital media content and multimedia databases, as well as knowledge engineers and domain experts from different applied disciplines with potential in multimedia data mining. The new theme of this year's workshop highlights the integration of multimedia in people's daily lives. With the rapid growth of mobile devices and personalized data, we wanted the workshop to focus on mining large scale rich content which is abundant in today's networked societies. Vast amount of multimedia data are produced, shared, and accessed everyday in various social platforms. These multimedia objects (images, videos, texts, tags, etc.) represent rich, multifaceted recordings of human behavior in the networked society, which lead to a range of social applications such as consumer behavior forecasting, social driven advertising / business, local knowledge discovery (e.g., for tourism or shopping), detection of emergent news events and trends, and so on. In addition to techniques for mining single media items, all of these applications require new methods for extracting robust features and discovering stable relationships among different media modalities and the users, in a dynamic, social context rich, and likely noisy environment. Multimedia is designed to stimulate the human senses beyond text. Using the rich content in multiple media types, we can capture the full experience of an event. Mining the data within media rich platforms and immersive environments (such as online communities, blogs, social networks, and virtual worlds) integrates the digital and physical experiences and information. The aim of the MDMKDD'13 workshop is to explore the role that multimedia data mining can play to enhance virtual society experiences.

User dynamics in online social networks have a significant impact not only on the online community but also on real-world activities. Therefore, understanding online user dynamics has become a significant issue. The wave equation-based model for online social networks (called the oscillation model) is a theoretical model proposed to describe user dynamics in online social networks. This model can be used to understand the relationship between explosive user dynamics and the structure of social networks. However, since the oscillation model was introduced as a purely theoretical model of social networks, it is necessary to confirm whether the model describes real phenomena correctly or not. In this paper, we first show a prediction from the oscillation model; the low-frequency oscillation mode of user dynamics will be dominant when the structure of online social networks changes so that user dynamics is activated. To verify the predictions with actual data, we show spectral analyses of both the log data of posts on an electronic bulletin board site and the frequency data of word search from Google Trends. The results support the predictions from the theoretical model.

The advent of Online Social Networks (OSNs) has offered the opportunity to study the dynamics of information spread and influence propagation at a huge scale. Considerable research has focused on the social influence phenomenon and its impact on OSNs. Social influence plays a crucial role in shaping people behavior and affecting human decisions in various domains.In this paper, we study the impact of social influence on offline dynamics to study human real-life behavior. We introduce Social Influence Deep Learning (SIDL), a framework that combines deep learning with network science for modeling social influence and predicting human behavior on real-world activities, such as attending an event or visiting a location. We propose different approaches at varying degree of network connectivity with the objective of facing two typical challenges of deep learning: interpretability and scalability.We validate and evaluate our approaches using data from Plancast, an Event-Based Social Network, and Foursquare, a Location-Based Social Network. Finally, we explore the usage of different deep learning architectures, and we discuss the correlation between social influence and users privacy presenting results and some notes of caution about the risks of sharing sensitive data.

Online Social Network (OSN) is like a virtual community where people build social networks and relations with one another. The open access to the Internet has increased the growth of OSN which has attracted intruders to exploit the weaknesses of the Internet and OSN to their own gain. The rise in the usage of OSN has posed security threats to OSN users as they share personal and sensitive information online which could be exploited by these intruders by creating profiles to carry out a series of malicious activities on the social network. In fact, it is no gain saying that the intent of creating fake accounts has adverse effect and the Internet has made it quite easy to concede one’s identity; and this makes it difficult to detect fake accounts as they try to imitate real accounts. In this study, a model that can accurately identify fake profiles in OSN which uses Natural Language Processing Technique to eliminate or reduce the size of the dataset thereby improving the overall performance of the model was proposed. Principal Component Analysis was used for appropriate feature selection. After extraction, six attributes/features that influenced the classifier were found. Support Vector Machine (SVM), Naïve Bayes and Improved Support Vector Machine (ISVM) were used as Classifiers. ISVM introduced a penalty parameter to the standard SVM objective function to reduce the inequality constraints between the slack variables. This gave a better result of 90% than the SVM and Naïve Bayes which gave 77.4% and 77.3% respectively.

Advances in Web 2.0 technologies have led to unstructured information overload in the complex and multidimensional datasets that originate in social networks. This overload can diminish the quality of web-related services such as recommender systems in the social web and can overwhelm users with irrelevant information. Such overload indicates the need to design and develop efficient and accurate information management and retrieval systems in social networks and anticipate recommendations in the semantic context of user interest. Community detection in social networks can help identify higher-order structures that unveil insight into networks and their functional organization and reduce irrelevant information received by the user. In this study, the application of community detection in online social networks is investigated within the framework of topic discovery-based user link identification and is consequently used to uncover implicit user communities (ad hoc communities). In the proposed approach, we use a graph-based information extraction technique that provides for a personalized information retrieval (recommender) system. We hypothesize that the ad hoc communities of users sharing similar interests embedded in a folksonomy-based social network can be identified by overlapping tag clusters in the tag concept hierarchy. Our methodology incorporates the novel information extraction techniques of topic modeling for topic extraction (feature extraction), user profile modeling for user profile extraction, and community extraction from the social graph, modeled in a framework to derive relevant ad hoc user communities of social interest from folksonomy data. Our experimental results demonstrate an accuracy of 70–98 % in community detection using data obtained from CiteULike®.

Chapter 1 - Why Design to Thrive?

With the recent surge of location-based social networks (LBSNs), such as Foursquare and Facebook Places, huge digital footprints of people's locations, profiles, and online social connections become accessible to service providers. Unlike social networks (e.g., Flickr, Facebook) that have explicit groups for users to subscribe to or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection and profiling approaches are needed. In the meantime, the diversity of people's interests and behaviors when using LBSNs suggests that their community structures overlap. In this paper, based on the user check-in traces at venues and user/venue attributes, we come out with a novel multimode multi-attribute edge-centric coclustering framework to discover the overlapping and hierarchical communities of LBSNs users. By employing both intermode and intramode features, the proposed framework is not only able to group like-minded users from different social perspectives but also discover communities with explicit profiles indicating the interests of community members. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset.

Location-based social networks (LBSNs) are a recent phenomenon for sharing a presence at everyday locations with others and have the potential to give new insights into human behaviour. To date, due to barriers in data collection, there has been little research into how our personality relates to the categories of place that we visit. Using the Foursquare LBSN, we have released a web-based participatory application that examines the personality characteristics and checkin behaviour of volunteer Foursquare users. Over a four-month period, we examine the behaviour and the “Big Five” personality traits of 174 anonymous users who had collectively checked in 487,396 times at 119,746 venues. Significant correlations are found for Conscientiousness, Openness and Neuroticism. In contrast to some previous findings about online social networks, Conscientiousness is positively correlated with LBSN usage. Openness correlates mainly with location-based variables (average distance between venues visited, venue popularity, number of checkins at sociable venues). For Neuroticism, further negative correlations are found (number of venues visited, number of sociable venues visited). No correlations are found for the other personality traits, which is surprising for Extroversion. The study concludes that personality traits help to explain individual differences in LBSN usage and the type of places visited.

Understanding the Design of Mobile Social Networking

Virtual friend recommendations in virtual worlds

The introduction and use of online social media networks in education has provided a variety of unique methodologies in support of teaching, learning, and knowledge gathering. The presence of these networks has created opportunities to hear the voice of the teacher. This study explores how teachers and officials from a rural district in South Africa used the WhatsApp platform as a virtual community of practice to aid in monitoring and support after attending a professional development programme. The data used in this study was collected from the WhatsApp conversations held amongst teachers and officials. This data was analysed within the conceptual framework of social learning and social networking. The findings derived from this study show that the effective use of an online social media network to support a virtual community of practice is dependent on the participants’ awareness of the context within which the community exists and the willingness of the participants to accept differing views and opinions. Keywords: community of practice; Millennium Development Goals; social learning; social media; social networking; teacher professional development; virtual community of practice; WhatsApp

With the proliferation of social network services (e.g., Facebook, Twitter, and Instagram), identifying cohesive subgraphs in networks is a key and fundamental problem to understand users' activity. Recently this problem has received extensive research attention. Identifying cohesive subgraph structures in networks has many applications such as recommendation, detecting fraudsters, event organization, and graph analysis. In this thesis, we study three important problems of finding cohesive subgraphs. Our studies include finding a query-based cohesive subgraph search in social networks and location-based social networks, and detecting all cohesive subgraphs in attributed bipartite networks. In our study of Density Modularity based Community Search (DMCS), we propose the modularity-based community search problem in networks. Modularity is a widely used measure of the structure of networks which checks the strength of partition of a network into communities. Most of the existing community search models only focus on the internal cohesiveness of a community. However, a high-quality community often has high modularity, which implies dense connections inside communities and sparse connections to the nodes outside the community. Hence, we conduct a pioneer study on searching for a community with high modularity. We point out that while modularity has been used in community detection (unrelated to query nodes), its application in community search (related to query nodes) brings in different challenges named free-rider effect and resolution limit problems. Both problems indicate that intrinsically optimizing the modularity can fail to find a small-sized community as a result for the community search problem. We mitigate these problems by designing a new graph modularity function named Density Modularity. To the best of our knowledge, this is the first work on the community search problem by using the graph modularity. The community search based on the density modularity, termed as DMCS, is to find a community in a social network that contains all the query nodes and has high density modularity. We prove that the DMCS problem is NP-hard, and thus there is no exact algorithm that is scalable to large graphs. To efficiently address DMCS, we present new algorithms that run in log-linear time to the graph size. We conduct extensive experimental studies in real-world and synthetic networks, which offer insights into the efficiency and effectiveness of our approximate algorithms. In our study of GeoSocial Community Search(GCS) problem, we propose the community search problem in location-based social networks. Specifically, we aim at finding a social community and a location cluster that are densely connected in a location-based social network. GCS can be useful for finding marketing targets and user/location recommendations. To the best of our knowledge, this is the first work to find a social community and a location cluster that are densely connected from location-based social networks. We prove that the GCS problem is NP-hard and is not in APX, unless P = NP. To solve GCS problem, we propose three effective and efficient algorithms: Core-based Basic Algorithm, Top-down Greedy Removing Algorithm, and Bottom-up Greedy Expansion Algorithms. Finally, we report extensive experimental studies that offer insights into the efficiency and effectiveness of the proposed solutions. In our study of Attributed Bipartite Co-clustering (ABC), we propose the co-clustering problem in attributed bipartite networks. Co-clustering in bipartite networks is a fundamental and important problem. Specifically, we unify two main concepts: (i) bipartite modularity optimization, and (ii) attribute cohesiveness. To the best of our knowledge, this is the first work to find a set of co-clusters while considering the attribute cohesiveness. We prove that ABC problem is NP-hard and propose three effective and efficient algorithms: (1) Top-Down Algorithm; (2) Bottom-Up Algorithm; and (3) Group-based Matching Algorithm. Finally, extensive experimental results on real-world attributed bipartite networks demonstrate the efficiency and effectiveness of our algorithms.

In location-based social networks (LBSNs), users implicitly interact with each other by visiting places, issuing comments and/or uploading photos. These heterogeneous interactions convey the latent information for identifying meaningful user groups, namely social communities, which exhibit unique location-oriented characteristics. In this work, we aim to detect and profile social communities in LBSNs by representing the heterogeneous interactions with a multimodality nonuniform hypergraph. Here, the vertices of the hypergraph are users, venues, textual comments or photos and the hyperedges characterize the k -partite heterogeneous interactions such as posting certain comments or uploading certain photos while visiting certain places. We then view each detected social community as a dense subgraph within the heterogeneous hypergraph, where the user community is constructed by the vertices and edges in the dense subgraph and the profile of the community is characterized by the vertices related with venues, comments and photos and their inter-relations. We present an efficient algorithm to detect the overlapped dense subgraphs, where the profile of each social community is guaranteed to be available by constraining the minimal number of vertices in each modality. Extensive experiments on Foursquare data well validated the effectiveness of the proposed framework in terms of detecting meaningful social communities and uncovering their underlying profiles in LBSNs.