Chih-Ya Shen

On socio-spatial group query for location-based social networks

Challenges faced in organizing impromptu activities are the requirements of making timely invitations in accordance with the locations of candidate attendees and the social relationship among them. It is desirable to find a group of attendees close to a rally point and ensure that the selected attendees have a good social relationship to create a good atmosphere in the activity. Therefore, this paper proposes Socio-Spatial Group Query (SSGQ) to select a group of nearby attendees with tight social relation. Efficient processing of SSGQ is very challenging due to the tradeoff in the spatial and social domains. We show that the problem is NP-hard via a proof and design an efficient algorithm SSGSelect, which includes effective pruning techniques to reduce the running time for finding the optimal solution. We also propose a new index structure, Social R-Tree to further improve the efficiency. User study and experimental results demonstrate that SSGSelect significantly outperforms manual coordination in both solution quality and efficiency.

S-AKA: A Provable and Secure Authentication Key Agreement Protocol for UMTS Networks

The authentication and key agreement (AKA) protocol of Universal Mobile Telecommunication System (UMTS), which is proposed to solve the vulnerabilities found in Global System for Mobile Communications (GSM) systems, is still vulnerable to redirection and man-in-the-middle attacks. An adversary can mount these attacks to eavesdrop or mischarge the subscribers in the system. In this paper, we propose a secure AKA (S-AKA) protocol to cope with these problems. The S-AKA protocol can reduce bandwidth consumption and the number of messages required in authenticating mobile subscribers. We also give the formal proof of the S-AKA protocol to guarantee its robustness.

Mining Online Social Data for Detecting Social Network Mental Disorders

An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental factors considered in standard diagnostic criteria (questionnaire) cannot be observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMDbased Tensor Model (STM) to improve the performance. Our framework is evaluated via a user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results show that SNMDD is promising for identifying online social network users with potential SNMDs.

Maximizing Friend-Making Likelihood for Social Activity Organization

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by modeling both existing friendships and the likelihood of new friend making. To find a set of attendees for socialization activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships and the objective function on the likelihood of friend making. We prove that HMGF is NP-Hard, and no approximation algorithm exists unless \(P=NP\). We then propose an error-bounded approximation algorithm to efficiently obtain the solutions very close to the optimal solutions. We conduct a user study to validate our problem formulation and perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm.

Collaborative and Distributed Search System with Mobile Devices

With the advances of communications, computing, and positioning technologies, mobile devices have been regarded as mobile computing platforms for various kinds of location-based and human-computation services. However, most existing applications regard each device as a sensor or focus on services with the computation on a single device. In contrast, this paper leverages a group of mobile devices as a collaborative and distributed search platform. Specifically, we propose a search system with mobile devices for rescue and patrol operations. The system utilizes mobile devices to find and assign the search route to each searcher in a collaborative and distributed manner. Given the roads to be searched in an area and the candidate start locations, our system minimizes the time required to search the whole area and guarantees that each road will be searched at least once. We first formulate the k-Person Search Problem for k mobile devices and prove that the problem is NP-Hard. To find the optimal solutions, we propose a centralized algorithm for a special case and an Integer Linear Programming formulation for general cases. We also devise an approximation algorithm. The algorithms can be used to dispatch the searchers before the operation starts. Moreover, to support online adaptation, we formulate the Path Refinement Problem for path exchange among searchers and propose a distributed algorithm to adaptively adjust the paths after the search starts. We also implement the proposed algorithms in mobile devices as a collaborative and distributed search system and demonstrate the efficiency of our algorithms with computation simulations and field trials.

Socio-Spatial Group Queries for Impromptu Activity Planning

The development and integration of social networking services and smartphones have made it easy for individuals to organize impromptu social activities anywhere and anytime. Main challenges arising in organizing impromptu activities are mostly due to the requirements of making timely invitations in accordance with the potential activity locations, corresponding to the locations of, and the relationships among the candidate attendees. Various combinations of candidate attendees and activity locations create a large solution space. Thus, in this paper, we propose Multiple Rally-Point Social Spatial Group Query (MRGQ), to select an appropriate activity location for a group of nearby attendees with tight social relationships. We first consider a special case of MRGQ, namely the Socio-Spatial Group Query (SSGQ), to determine a set of socially acquainted attendees while minimizing the total spatial distance to a specific activity location. We prove that SSGQ is NP-hard and formulate an Integer Linear Programming optimization model for SSGQ. We then develop an efficient algorithm, called SSGS, which employs effective pruning techniques to reduce the running time to determine the optimal solution. Moreover, we propose a heuristic algorithm for SSGQ to efficiently produce good solutions. We next consider the more general MRGQ. Although MRGQ is NP-hard, the number of attendees in practice is usually small enough such that an optimal solution can be found efficiently. Therefore, we first propose an Integer Linear Programming optimization model for MRGQ. We then design an efficient algorithm, called MAGS, which employs effective search space exploration and pruning strategies to reduce the running time for finding the optimal solution. We also propose to further optimize efficiency by indexing the potential activity locations. A user study demonstrates the strength of using SSGS and MAGS over manual coordination in terms of both solution quality and efficiency. Experimental results on real datasets show that our algorithms can process SSGQ and MRGQ efficiently and significantly outperform other baseline algorithms, including one based on the commercial parallel optimizer IBM CPLEX.

Revenue maximization for telecommunications company with social viral marketing

Viral marketing, a marketing strategy that leverages the influence power in intimate relationship, has become more prevalent due to the popularity of online social networking services in recent years. Consumers are more likely to make a purchase based on social media referrals. Since marketing through social media and traditional channels may target on different audiences, how to maximize the revenue of a telecommunications company by employing different advertising ways and selecting initial users for advertisements is a critical problem. Therefore, in this paper, we formulate a new research problem, namely Cost-Aware Multi-wAy Influence maXimization (CAMAIX) to address the need mentioned above. We design a 1/2-approximation algorithm with various pruning and budget allocation strategies to solve CAMAIX efficiently. We conduct extensive experiments on a large-scale real dataset from a telecommunications company. The results show that our proposed algorithm outperforms the baseline algorithms in both solution quality and efficiency.

On Pattern Preserving Graph Generation

Real datasets always play an essential role in graph mining and analysis. However, nowadays most available real datasets only support millions of nodes. Therefore, the literature on Big Data analysis utilizes statistical graph generators to generate a massive graph (e.g., billions of nodes) for evaluating the scalability of an algorithm. Nevertheless, current popular statistical graph generators are properly designed to preserve only the statistical metrics, such as the degree distribution, diameter, and clustering coefficient of the original social graphs. Recently, the importance of frequent graph patterns has been recognized in the various works on graph mining, but unfortunately this crucial criterion has not been noticed in the existing graph generators. To address this important need, we make the first attempt to design a Pattern Preserving Graph Generation (PPGG) algorithm to generate a graph including all frequent patterns and three most popular statistical parameters: degree distribution, clustering coefficient, and average vertex degree. The experimental results show that PPGG, which we have released as a free download, is efficient and able to generate a billion-node graph in approximately 10 minutes, much faster than the existing graph generators.

On Finding Socially Tenuous Groups for Online Social Networks

Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group (MkTG), to find a socially tenuous group from online social networks. We prove that MkTG is NP-Hard and inapproximable within any ratio in arbitrary graphs but polynomial-time tractable in threshold graphs. Two algorithms, namely TERA and TERA-ADV, are designed to exploit graph-theoretical approaches for solving MkTG on general graphs effectively and efficiently. Experimental results on seven real datasets manifest that the proposed algorithms outperform existing approaches in both efficiency and solution quality.

Forming Online Support Groups for Internet and Behavior Related Addictions

While online social networks have become a part of many peoples daily lives, Internet and social network addictions (ISNAs) have been noted recently. With increased patients in addictive Internet use, clinicians often form support groups to help patients. This has become a trend because groups organized around therapeutic goals can effectively enrich members with insight and guidance while holding everyone accountable along the way. With the emergence of online social network services, there is a trend to form support groups online with the aid of mental health professionals. Nevertheless, it becomes impractical for a psychiatrist to manually select the group members because she faces an enormous number of candidates, while the selection criteria are also complicated since they span both the social and symptom dimensions. To effectively address the need of mental healthcare professionals, this paper makes the first attempt to study a new problem, namely Member Selection for Online Support Group (MSSG). The problem aims to maximize the similarity of the symptoms of all selected members, while ensuring that any two members are unacquainted to each other. We prove that MSSG is NP-Hard and inapproximable within any ratio, and design a 3-approximation algorithm with a guaranteed error bound. We evaluate MSSG via a user study with 11 mental health professionals, and the results manifest that MSSG can effectively find support group members satisfying the member selection criteria. Experimental results on large-scale real datasets also demonstrate that our proposed algorithm outperforms other baselines in terms of solution quality and efficiency.