Information overload in structured data

Information overload refers to the difficulty of making decisions caused by too much information. In this dissertation, we address information overload problem in two separate structured domains, namely, graphs and text. Graph kernels have been proposed as an efficient and theoretically sound approach to compute graph similarity. They decompose graphs into certain sub-structures, such as subtrees, or subgraphs. However, existing graph kernels suffer from a few drawbacks. First, the dimension of the feature space associated with the kernel often grows exponentially as the complexity of sub-structures increase. One immediate consequence of this behavior is that small, non-informative, sub-structures occur more frequently and cause information overload. Second, as the number of features increase, we encounter sparsity: only a few informative sub-structures will co-occur in multiple graphs. In the first part of this dissertation, we propose to tackle the above problems by exploiting the dependency relationship among sub-structures. First, we propose a novel framework that learns the latent representations of sub-structures by leveraging recent advancements in deep learning. Second, we propose a general smoothing framework that takes structural similarity into account, inspired by state-of-the-art smoothing techniques used in natural language processing. Both the proposed frameworks are applicable to popular graph kernel families, and achieve significant performance improvements over state-of-the-art graph kernels. In the second part of this dissertation, we tackle information overload in text. We first focus on a popular social news aggregation website, Reddit, and design a submodular recommender system that tailors a personalized frontpage for individual users. Second, we propose a novel submodular framework to summarize videos, where both transcript and comments are available. Third, we demonstrate how to apply filtering techniques to select a small subset of informative features from virtual machine logs in order to predict resource usage

Big Networks: Analysis and Optimal Control

The study of networks has seen a tremendous breed of researches due to the explosive spectrum of practical problems that involve networks as the access point. Those problems widely range from detecting functionally correlated proteins in biology to finding people to give discounts and gain maximum popularity of a product in economics. Thus, understanding and further being able to manipulate/control the development and evolution of the networks become critical tasks for network scientists. Despite the vast research effort putting towards these studies, the present state-of-the-arts largely either lack of high quality solutions or require excessive amount of time in real-world `Big Data\u27 requirement. This research aims at affirmatively boosting the modern algorithmic efficiency to approach practical requirements. That is developing a ground-breaking class of algorithms that provide simultaneously both provably good solution qualities and low time and space complexities. Specifically, I target the important yet challenging problems in the three main areas: Information Diffusion: Analyzing and maximizing the influence in networks and extending results for different variations of the problems. Community Detection: Finding communities from multiple sources of information. Security and Privacy: Assessing organization vulnerability under targeted-cyber attacks via social networks

An exploratory study of recharging mechanisms in a shared service context

It is claimed that the transformation of business support services through some form of ‘externalization’ mechanism such as the shared service centre (SSC) model can drive down costs and enhance functionality. A key characteristic of the SSC’s rationale is its ability change by replicate the characteristics of third-party outsourcing, whilst also retaining overall management control within the boundaries of the organization. In such a market-oriented model, it should follow that a key feature of an SSC is the recharging of its costs to its customers, the business facing operational units. Yet, in offering a hybrid solution combining characteristics of market and hierarchy, it could be expected that the recharging for support services will seek to combine the tenets of both market-orientated transfer pricing with cost allocation methods traditionally associated with internal cost centres. The motivation of this study is the increasing prevalence of the SSC model amongst large organizations in the face of a relative paucity of contemporary literature on transfer pricing and cost allocation in new organizational forms, especially the SSC model. The central theme of this study is to explore the recharging mechanisms applied by different SSCs and their organizational effects in terms of balancing market coordination between the SSC and its customers and top management control. Three case studies were undertaken to explore how SSCs recharge the costs for the finance function as a business support services and the effects on managerial behaviours of both the SSC and business units. Drawing on theory of organizational structure, transaction cost economics (TCE) and agency theory, a conceptual framework was constructed to guide the analysis of the empirical evidence. The findings of this study include: 1) The choice of recharging method can be either transfer pricing approach (direct recharge) or cost allocation (indirect recharge), contingent upon; the governance orientation of each organization, asset specificity and extent of transactions, uncertainty and opportunism and bounded rationality. 2) Mandating the choice of recharging mechanism given the asymmetric bargaining power between the SSC and the head office could cause agency problems within the organizations, although this could be mitigated by transparency of information, appropriate coordination mechanisms, and performance measurement based on mutually agreed budget targets. 3) Recharging mechanism is fluid over time and in one of the cases (i.e. DHL Express) it is found that the transfer pricing can be used in the first stages of SSC implementation to drive change, but on maturity there is a reversion to broad brush cost allocation to better enable overall system optimization with reduced transaction costs.</div

Influence Maximization Mining for Competitive Social Networks

Influence maximization (IM) is one of the fundamental problems in the area of influence propagation in social networks. Recent studies in influence maximization have primarily focused on the diffusion of single influence. In this thesis, we study the problem under a new diffusion model named Competing General Threshold (CGT) model, which discovers k most influential nodes as early adopters of technology A (e.g., Apple) in a market where a competing technology B (e.g., Blackberry) already exists along with a set of early adopters of technology B. To solve IM under the diffusion of two influences, we first define the CGT diffusion model, then estimate both A and B influence probabilities by using Maximum-Likelihood Estimation from Twitter networks. Next, we propose a new algorithm named cgtMineA to find k influential A-seeds under the CGT model. Experimental results on Twitter networks show that our approach outperforms CELF