Chung-Kuang Chou

Distributed algorithms for k-truss decomposition

k-truss, a type of cohesive subgraphs of a network, is an important measure for a social network graph. However, with the emergence of large online social networks, the running time of the traditional batch algorithms for k-truss decomposition is usually prohibitively long on such a graph with billions of edges and millions of vertices. Moreover, the size of a graph becomes too large to load into the main memory of a single machine. Currently, cloud computing has become an imperative way to process the big data. Thus, our aim is to design a scalable algorithm of k-truss decomposition in the scenario of cloud computing. In this paper, we first improve the existing distributed k-truss decomposition in the MapReduce framework. We then propose a theoretical basis for k-truss and use it to design an algorithm based on graph-parallel abstractions. Our experiment results show that our method in the graph-parallel abstraction significantly outperforms the methods based on MapReduce in terms of running time and disk usage.

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.

Cluster cascades: infer multiple underlying networks using diffusion data

Information diffusion and virus propagation are the fundamental processes often taking place in networks. The problem of devising a strategy to facilitate or block such process has received a considerable amount of attention. A major challenge therein is that the underlying network of diffusion is often hidden. Most researchers dealing with this issue assume only one underlying network over which cascades spread. However, in the real world, whether the transmission pathways of a contagion, a piece of information, emerge or not depends on many factors, such as the topic of the information and the time when the information is first mentioned. In our opinion, it is impractical to model the diffusion processes by using only a single network when information is of all kind and diffuses in different underlying topic-specific networks. In this paper, we formulate a problem of K-network inference, inferring K underlying diffusion networks, based on a proposed probabilistic generative mixture model that models the generation of cascades. We further propose an algorithm that could cluster similar cascades and infer the corresponding underlying network for each cluster in the Expectation-Maximization framework. Finally, in experiments, we show that our algorithm could cluster cascades and infer the underlying networks effectively.

Influence Maximization for Complementary Goods: Why Parties Fail to Cooperate?

We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each others product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existence of competitive/complementary products in the network. Finally, we compare the TOPBOSS with different heuristic algorithms in real data and demonstrate the merits of TOPBOSS.

Multiple Factors-Aware Diffusion in Social Networks

Information diffusion is a natural phenomenon that information propagates from nodes to nodes over a social network. The behavior that a node adopts an information piece in a social network can be affected by different factors. Previously, many diffusion models are proposed to consider one or several fixed factors. The factors affecting the adoption decision of a node are different from one to another and may not be seen before. For a different scenario of diffusion with new factors, previous diffusion models may not model the diffusion well, or are not applicable at all. In this work, our aim is to design a diffusion model in which factors considered are flexible to extend and change. We further propose a framework of learning parameters of the model, which is independent of factors considered. Therefore, with different factors, our diffusion model can be adapted to more scenarios of diffusion without requiring the modification of the diffusion model and the learning framework. In the experiment, we show that our diffusion model is very effective on the task of activation prediction on a Twitter dataset.

Query-oriented Graph Clustering

There are many tasks including diversified ranking and social circle discovery focusing on the relationship between data as well as the relevance to the query. These applications are actually related to query-oriented clustering. In this paper, we firstly formulate the problem, query-oriented clustering, in a general form and propose the two measures, query-oriented normalized cut (QNCut) and cluster balance to evaluate the results for query-oriented clustering. We develop a model, query-oriented graph clustering (QGC), that combines QNCut and the balance constraint based on cluster balance in a quadratic form. In the experiments, we show that QGC achieves promising results on improvement in query-oriented clustering and social circle discovery.

Context-aware daily activity summarization with adaptive transmission

With the growth of innovative wearable and mobile devices, smart applications in daily life become more complicated. Most of these applications offload all data from wearable and mobile devices to remote servers to overcome the limitations of device resources. However, offloading all the data, especially multimedia contents, requires a large number of network resources and may result in the dissatisfaction of users who use such applications. To alleviate the problem, we propose a practical system architecture which includes an adaptive transmission mechanism to reduce the network bandwidth usage. We design and implement a multimedia application, which generates a diary-like daily activity summarization, with the proposed system architecture to verify the feasibility. In the experiment with four participants wearing the wearable camera for fourteen days, the results show that over 89% of the overall bandwidth usage can be reduced with sacrificing 11% of the server-side performance via the proposed adaptive transmission mechanism.

On Package Organization for Willingness Satisfaction in Social Networks

Studies show that both the personal preference and social tightness between friends play important roles in the decision process of activity participation for a person. Considering the preference of a person and the social tightness among friends, in this work, we formulate a new research problem, called Package Organization for Willingness satisfaction (POWA), to effectively select items into a package that can be adopted by the most users. Efficiently obtaining the optimal package and the corresponding group of users under the setting of POWA is very challenging, as we prove that POWA is NP-hard. Aiming to strike a balance between the quality of solutions and the time needed for computation, we propose algorithm Intermediate Package Organization with Social and Preference Pruning (IPOSPP) to obtain good solutions efficiently. We conduct an extensive performance evaluation on four real datasets to demonstrate the performance of the proposed algorithm.