The structure of information pathways in a social communication network

Clinical Orthopaedics and Related Research, Volume abs/0806.3201, 2008, Pages 435-443.

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temporal dynamicpure social network topologytemporal measureunderlying social networkembedded edgeMore(10+)
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We study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large university over a two-year period

Abstract:

Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large university over a two-year period. We formulate ...More

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Introduction
  • Large social networks serve as conduits for communication and the flow of information [2, 11]; but information only spreads on these networks as a result of discrete communication events—such as e-mail or text messages, conversations, meetings, or phone calls— that are distributed non-uniformly over time [10, 32].
  • Recent research working with such datasets has primarily studied communication of an event-driven nature, looking at communication within a social network triggered by a particular event or activity; such investigations have typically focused on communication events that ripple through many nodes over short time-scales following the triggering event
  • Examples of this include cascades of e-mail recommendations for products [21], cascades of references among bloggers [3, 13, 23], the spread of e-mail chain letters [24], and the search for distant targets in a social network [8, 29]
Highlights
  • Large social networks serve as conduits for communication and the flow of information [2, 11]; but information only spreads on these networks as a result of discrete communication events—such as e-mail or text messages, conversations, meetings, or phone calls— that are distributed non-uniformly over time [10, 32]
  • Recent research working with such datasets has primarily studied communication of an event-driven nature, looking at communication within a social network triggered by a particular event or activity; such investigations have typically focused on communication events that ripple through many nodes over short time-scales following the triggering event
  • While the communication skeleton is a fairly dense graph, we find that the backbones and the aggregate backbone are surprisingly sparse — 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
  • The basic definitions of social network analysis have been primarily built on graph-theoretic foundations rooted in unweighted graphs
  • We find that adapting the notion of vector-clocks from the analysis of distributed systems provides a principled way to measure how “out-of-date” one person is with respect to another, and we find that the sparse subgraph 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 hubs, and the strength of weak ties
Conclusion
  • The basic definitions of social network analysis have been primarily built on graph-theoretic foundations rooted in unweighted graphs.
  • The authors find that adapting the notion of vector-clocks from the analysis of distributed systems provides a principled way to measure how “out-of-date” one person is with respect to another, and the authors find that the sparse subgraph 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 hubs, and the strength of weak ties
  • This style of analysis allows them to study the effects on information flow as nodes vary the rate at which they communicate with others in the network, ranging from strategies in which communication is concentrated on heavily-used edges to those in which it is leveled out across many edges
Summary
  • Introduction:

    Large social networks serve as conduits for communication and the flow of information [2, 11]; but information only spreads on these networks as a result of discrete communication events—such as e-mail or text messages, conversations, meetings, or phone calls— that are distributed non-uniformly over time [10, 32].
  • Recent research working with such datasets has primarily studied communication of an event-driven nature, looking at communication within a social network triggered by a particular event or activity; such investigations have typically focused on communication events that ripple through many nodes over short time-scales following the triggering event
  • Examples of this include cascades of e-mail recommendations for products [21], cascades of references among bloggers [3, 13, 23], the spread of e-mail chain letters [24], and the search for distant targets in a social network [8, 29]
  • Conclusion:

    The basic definitions of social network analysis have been primarily built on graph-theoretic foundations rooted in unweighted graphs.
  • The authors find that adapting the notion of vector-clocks from the analysis of distributed systems provides a principled way to measure how “out-of-date” one person is with respect to another, and the authors find that the sparse subgraph 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 hubs, and the strength of weak ties
  • This style of analysis allows them to study the effects on information flow as nodes vary the rate at which they communicate with others in the network, ranging from strategies in which communication is concentrated on heavily-used edges to those in which it is leveled out across many edges
Funding
  • This research was supported in part by the Institute for Social and Economic Research and Policy at Columbia University, the Institute for the Social Sciences at Cornell University, the James S
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