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Quantifying Social Group Evolution Gergely Palla, Albert-Laszlo Barabasi, and Tamas Vicsek Nature Vol 446 April 2007 Presented by: Liang Ding Finance Department,

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Presentation on theme: "Quantifying Social Group Evolution Gergely Palla, Albert-Laszlo Barabasi, and Tamas Vicsek Nature Vol 446 April 2007 Presented by: Liang Ding Finance Department,"— Presentation transcript:

1 Quantifying Social Group Evolution Gergely Palla, Albert-Laszlo Barabasi, and Tamas Vicsek Nature Vol 446 April 2007 Presented by: Liang Ding Finance Department, KSU

2 Introduction Complex community structure Social and communication network is subject to constant evolution The knowledge of the mechanisms governing the underlying community dynamics is limited

3 Intro cond Aim: to uncover basic relationships characterizing community evolution An algorithm based on clique percolation

4 data 1. the monthly list of articles in the Cornell University Library e-print condensed matter achieve spanning 142 months, with over 30,000 authors; 2. the record of phone calls of a mobile phone company spanning 52 weeks, 4 million users.

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6 To check overlap the average weight of the links inside communities the average weight of the inter- community links; For co-author is 2.9; for phone-call is 5.9 Indicating that the intensity of collaboration /communication within a group is significantly higher than with contacts belonging to a different group

7 To check homogeneity

8 The basic events in the life of a community

9 Basic quantities characterizing a community Size: s Age: Auto-correlation function: C(t)

10 Characteristic features of community evolution

11 The stationarity As the average correlation between subsequent states.

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13 The relationship between the lifetime, the stationarity and the community size

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15 The time evolution of four communities from the co-authorship network show: A typical small and stationary community undergoes minor changes, but lives for a long time; A large non-stationary community whose members change dynamically, resulting in significant fluctuations in both size and composition, has a quite extended lifetime

16 Could the inspection of a community itself predict its future? w out: the total weight of this member ’ s connections to outside of the community; w in: the total weight of this member ’ s connections to members belonging to the same community; Calculate the probability that the member will abandon the community as a function of the w out /(w in +w out ) ratio

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18 Take the idea from individuals to communities

19 summary Large groups persist for longer if they are capable of dynamically altering their membership; Small groups displays the opposite tendency; The time commitment of members to a given community can be used for estimating the community ’ s lifetime.


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