1. The Structure of Information Pathways in a Social Communication Network The Structure of Information Pathways in a Social Communication Network Presented By:Under the guidance of: Tingting Xu Augustin Chainterau Tingting Xu Augustin Chainterau

2. Paper Objective  Study the temporal dynamics of communication using on-line data  Give temporal notion of ‘distance’ and ‘vector – clocks’ to formulate a temporal measure which will provide structural insights  Define the network backbone to be the sub-graph consisting of edges on which information has the potential to flow the quickest

3. Why Construct New Model  Discrete communication distributed non-uniformly over time  Direct and indirect flow of information  Discussion about recent research - has studied communication of an event-driven nature  The properties of systemic communication arguably determine much about the rate at which people in the network remain up-to- date on information about each other

4. The Present Work  Systemic communication and information pathways  Propose a framework for analyzing systemic communication based on inferring structural measures from the potential for information to flow between different nodes  Out-of-date information  Indirect paths – triangle-inequality violation

5. The Present Work  Data used here have complete histories of communication events over long periods of time  Main datasets - complete set of anonymized e-mail logs among all faculty and staff at a large university over two years  Enron e-mail corpus  The complete set of user-talk communications among admins and high-volume editors on Wikipedia  Vector clocks introduced by Lamport and refined by Mattern  Network backbone

6. Vector Clocks and Latency

7. Latencies in Social Network Data  Consider only messages with at most c (ranging from 1 and 5) recipients  Focus on q-fraction of active e-mal users (Here q = 0.20)  For a time difference τ, we define the ball of radius τ around node v at time t, denoted B τ (v, t), to be the set of all nodes whose latency with respect to v at time t is ≤ τ days.  For fixed t, the distribution of ball-sizes over nodes can be studied using a function f t (τ ), defined as the median value of |B τ (v, t)| over all v

8. Open Worlds vs. Closed Worlds  Boundary specification problem – value of q-fraction [0, 1]

9. Quantifying the Strength of Weak Ties

10. Backbone Structures

14.  Density and node degrees of the backbone  The backbone H t and the aggregate backbone H* are surprisingly sparse related to a fairly dense communication skeleton G  This in other words, from the point of view of potential information flow, a significant majority of all edges in the social network are bypassed by faster indirected paths

15. Backbone Structures  Density and node degrees of the backbone  Considering the backbone also sheds further light on the role of high- degree nodes in the social network  High-degree nodes in the full communication skeleton G indeed have many incident edges in the aggregate backbone  However, the fraction of a node’s edges that are declared essential strictly decreases with degree.

16. Backbone Structures  The backbone balances between two qualitatively different kinds of information flow

17. Varying Speed of Communication

18.  THEOREM The delay minimization problem defined above is NP – complete  Sketch of the proof of this theorem is in the paper  Consider simple local rules by which individuals in a network might vary rates of communication so as to influence the potential for information flow

19. Load-leveling vs. Load-concentrating

21. Conclusions (I)  Make integral use of information about how nodes communicate over time  Develop structural measures based on the potential for information to flow  The sparse sub-graph of edges most essential to keeping people up-to- date – the backbone of the network – provides important structural insights that relate to embeddedness, the role of high-degree(i.e. hubs), and the strength of weak ties  Studied the effects on information flow as nodes vary the rate at which they communicate with others in the network using different strategies

22. Conclusions (II)  Discussions in other two datasets  The situations in sparsity of the aggregate and instantaneous backbones and the variation in node degrees are similar  Difference - the ‘core’ of active communicators is much smaller in both the Enron corpus and in Wikipedia, this makes the range of an edge in the unweighted communication skeleton harder to interpret and to correlate with other measures  Further investigation  the principles that govern the dynamics of different types of information  how these principles interact with the directed, weighted nature of social communication networks

23. Thank You Thank You