Largest IM Platform In China Tecents QQ

This case illustrates how Mainland China based Tencent Holdings Ltd., through focusing on its core competencies, has rapidly evolved into a technology powerhouse in the global Instant Messaging market. In just over a decade, the company has influenced and changed the communication methods and life-style of hundreds of millions of Chinese in China and around the world. Staying true to its core competencies, such as intense attention to innovation, efficiency, and customer responsiveness, the company successfully expanded its marketplace footprint from a locally-sighted, straight-forward telecommunications service provider into a complex, internet value-added service provider with a global reach. This case includes an introduction to the instant messaging industry, followed by a profile of the company. Also, an extensive analysis of Tencents core competencies is presented, along with a summary of the companys path towards internationalization

Treemaps as a Tool for Social Network Analysis *

We apply treemap technology in the analysis of large, social network datasets—principally for examining network subgroups. A treemap is graphically-based information and exploration tool which is used in diverse fields such as computer science, finance, and human-gene research; from our experience, we find treemaps also useful in the social network analysis setting. A treemap represents hierarchical and categorical data in a mosaic form containing embedded, rectangular shapes, where the size of each shape is germane. Treemap displays are especially helpful when examining data in an interactive mode (as opposed to a static or printed form). We have found that treemaps are a powerful tool for exploring large social-networks, particularly during the exploratory data analysis phase. Their use quickly leads to a thorough perspective of the holistic characteristics of the network and to easier identification of significant subgroups; both of these perspectives may otherwise remain hidden using traditional visualization techniques. In this report, we introduce treemap technology, first broadly, then, specifically how it can be applied to social network analysis. We also show how we have actually applied treemaps to an interactive study of a large, real-world dataset. As a result of our experiences, w

Relating network topology to the robustness of centrality measures

This paper reports on a simulation study of social networks that investigated how network topology relates to the robustness of measures of system-level node centrality. This association is important to understand as data collected for social network analysis is often somewhat erroneous and may—to an unknown degree—misrepresent the actual true network. Consequently the values for measures of centrality calculated from the collected network data may also vary somewhat from those of the true network, possibly leading to incorrect suppositions. To explore the robustness, i.e., sensitivity, of network centrality measures in this circumstance, we conduct Monte Carlo experiments whereby we generate an initial network, perturb its copy with a specific type of error, then compare the centrality measures from two instances. We consider the initial network to represent a true network, while the perturbed represents the observed network. We apply a six-factor full-factorial block design for the overall methodology. We vary several control variables (network topology, size and density, as well as error type, form and level) to generate 10,000 samples each from both the set of all possible networks and possible errors within the parameter space. Results show that the topology of th

Relating network topology to the robustness of centrality measures

Abstract: "This paper reports on a simulation study of social networks that investigated how network topology relates to the robustness of measures of system-level node centrality. This association is important to understand as data collected for social network analysis is often somewhat erroneous and may -- to an unknown degree -- misrepresent the actual true network. Consequently the values for measures of centrality calculated from the collected network data may also vary somewhat from those of the true network, possibly leading to incorrect suppositions. To explore the robustness, i.e., sensitivity, of network centrality measures in this circumstance, we conduct Monte Carlo experiments whereby we generate an initial network, perturb its copy with a specific type of error, then compare the centrality measures from two instances. We consider the initial network to represent a true network, while the perturbed represents the observed network. We apply a six-factor full-factorial block design for the overall methodology. We vary several control variables (network topology, size and density, as well as error type, form and level) to generate 10,000 samples each from both the set of all possible networks and possible errors within the parameter space. Results show that the topology of the true network can dramatically affect the robustness profile of the centrality measures. We found that across all permutations that cellular networks had a nearly identical profile to that of uniform-random networks, while the core-periphery networks had a considerably different profile. The centrality measures for the core-periphery networks are highly sensitive to small levels of error, relative to uniform and cellular topologies. Except in the case of adding edges, as the error increases, the robustness level for the 3 topologies deteriorate and ultimately converges.