By Przemyslaw Kazienko, Nitesh Chawla
This number of contributed chapters demonstrates a variety of functions inside of overlapping learn domain names: social media research and social community research. quite a few methodologies have been used in the twelve person chapters together with static, dynamic and real-time techniques to graph, textual and multimedia facts research. the subjects observe to acceptance computation, emotion detection, subject evolution, rumor propagation, overview of textual critiques, pal rating, 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 hard purposes is what makes this edited quantity of curiosity to researchers and scholars considering social media and social community analysis.
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We see that the listed degree and each of the sample indegrees (total, ranked, rank1 , rank2 , and rank3 ) show a heavy-tailed degree distribution. ) As one would expect, the rank1 , rank2 , and rank3 indegrees are smaller in an absolute sense. For any fixed k, rankk links are a scarce resource; only one such outgoing link is possible per user, so there are only ≈11 M such possible links in total. The remaining degree measures are effectively unlimited in the sense that each user can generate arbitrarily many outgoing links and nearly arbitrarily many outgoing ranked links (by lengthening her Top Friends list).
2 DelayFlow Centrality Commuter Flow Centrality The commuter flow centrality of a node i, C f (i), is defined as the number of commuters affected per hour when node i is down. We classify the affected commuters into three categories: • Commuters traveling from node i to other nodes; • Commuters traveling from other nodes to node i; and • Commuters traveling through node i. Hence, we define the commuter flow centrality of a node i to be: C f (i) = hi j + j∈V h ji + j∈V h jk (i) j∈V, j,k=i, j =k where h i j denotes the number of commuters from node i to node j per hour, and h jk (i) denotes the number of commuters from node j to node k through node i per hour.
Sg API’s allow us to determine the travel time by MRT train (expected) or bus (alternative) for each origin and destination station pair. Bus is chosen as it is the next most commonly used mode of public transportation. Given that we have altogether 89 stations in the MRT network, we use the APIs to derive tex p (i, j) and talt (i, j) time for all 89·88 = 7832 station pairs. To keep things simple, we assume that tex p (i, j)’s and talt (i, j)’s are independent of the time of the day. sg. Measuring Centralities for Transportation Networks Beyond Structures 31 5 Comparison of Centrality Measures Distribution of Degree, Closeness and Betweenness Centralities Figures 5, 6 and 7 show the distribution of the degree, closeness and betweenness centrality values of the stations in MRT network.