By Przemyslaw Kazienko, Nitesh Chawla
This choice of contributed chapters demonstrates quite a lot of purposes inside of overlapping learn domain names: social media research and social community research. a number of methodologies have been used in the twelve person chapters together with static, dynamic and real-time ways to graph, textual and multimedia information research. the themes follow to popularity computation, emotion detection, subject evolution, rumor propagation, review of textual critiques, good friend score, research of public transportation networks, diffusion in dynamic networks, research of individuals to groups of open resource software program builders, biometric template iteration in addition to research of consumer habit inside heterogeneous environments of cultural academic facilities. Addressing those difficult functions is what makes this edited quantity of curiosity to researchers and scholars curious about social media and social community analysis.
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Additional info for Applications of Social Media and Social Network Analysis
Many studies that involve social networks assume the second degrees (degrees of the neighbors) are known either because the degree distribution of agents contains high information. We are currently relaxing this assumption and trying to estimate the second degree distribution using bayesian probabilistic methods. We assume that each agent start the estimation with the same degree of its own . The degree distribution of a given social network is assumed to follow a power-law distribution and we let each agent apply power-law to the estimated second degree distribution.
Most centrality measures in social network analysis research unfortunately consider only topological structure of the network and are oblivious of transportation factors. This paper proposes new centrality measures that incorporate travel time delay and commuter flow volume. We apply the proposed measures on the Singapore’s subway network involving 89 stations and about 2 million commuter trips per day, and compare them with traditional topology based centrality measures. Several interesting insights about the network are derived from the new measures.
8 Commuter flow centrality C f distribution (weekday) Fig. 9 Commuter flow centrality distribution at selected time segments (weekday) Measuring Centralities for Transportation Networks Beyond Structures 33 Fig. 10 Time delay centrality Cdel distributions at selected time segments (weekday) Distribution of Time Delay Centrality Figure 10 depicts the time delay centrality distribution at different time durations of a weekday as the centrality. 4 showing that the alternative means of transportation can take between 50 to 240 % more than the expected time required.