Acquisition of Social Network Structure


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Acquisition of Social Network Structure

  • Jason J. Jones











  • How do we learn the structure of graphs?

  • How are graph structures represented in the mind?



  • Social networks  Graphs

  • Graphs are Grammars are Markov Chains



Hypotheses

  • Human subjects will acquire a network’s structure more quickly if it resembles a true human social network rather than an arbitrary network.

  • Human subjects will acquire a network’s structure more quickly if it is framed as a social network as opposed to the same network framed in some other manner (e.g. a computer or transport network.)

  • Some forms of representation of the network will lead to faster acquisition than others.



Hypotheses

  • Human subjects will acquire a network’s structure more quickly if it resembles a true human social network rather than an arbitrary network.

  • Human subjects will acquire a network’s structure more quickly if it is framed as a social network as opposed to the same network framed in some other manner (e.g. a computer or transport network.)

  • Some forms of representation of the network will lead to faster acquisition than others.



Hypotheses

  • Human subjects will acquire a network’s structure more quickly if it resembles a true human social network rather than an arbitrary network.

  • Human subjects will acquire a network’s structure more quickly if it is framed as a social network as opposed to the same network framed in some other manner (e.g. a computer or transport network.)

  • Some forms of representation of the network will lead to faster acquisition than others.



Experiment 1

  • 112 subjects

  • 2 (Graph, within) x 2 (Training, between)

  • 2AFC classification test trials

  • Interleaved training and test trials



Manipulation: Graph Type

  • Observable human social networks are scale-free.

    • Email Networks
      • Ebel, H., Mielsch, L.I., Bornholdt, S. (2002)
    • File-Sharing Networks
      • Wang, F., Moreno, Y., & Sun, Y. (2006)
    • Sex Networks
      • Liljeros, F., Edling, C. R., Amaral, L. A. N., Stanley, H. E., & Åberg, Y. (2001)


Manipulation: Graph Type

  • Random Graph

    • Erdős & Rényi (ER)




Manipulation: Graph Type

  • For to all those who have, more will be given, and they will have an abundance; but from those who have nothing, even what they have will be taken away.

  • Matthew 25:29



Manipulation Check



Manipulation: Training

  • Edge Training











Conclusions

  • Subjects acquire scale-free graph structure more quickly than random graph structure.

  • The difference between edge training and walk training is small or non-existent.



Hypotheses

  • Human subjects will acquire a network’s structure more quickly if it resembles a true human social network rather than an arbitrary network.

  • Human subjects will acquire a network’s structure more quickly if it is framed as a social network as opposed to the same network framed in some other manner (e.g. a computer or transport network.)

  • Some forms of representation of the network will lead to faster acquisition than others.



Experiment 2

  • 102 subjects

  • 2 (Graph, within) x 3 (Frame, between)

  • Frame

    • Social Network
    • Transport Network
    • Computer Network


Manipulation: Graph Type

  • Random Graph

    • Erdős & Rényi (ER)




















Conclusions

  • Subjects acquire scale-free graph structure more quickly than random graph structure. (Replicated)

  • Subjects were slower to acquire the structure of a computer network than a social or transport network.

  • Performance for computer networks was so poor, it diminished the size of the graph effect.



Experiment 2a

  • The same list of names used for social, transport and network frames.

  • 177 subjects

  • 2 (Graph, within) x 3 (Frame, between)

  • Frame

    • Social Network
    • Transport Network
    • Computer Network


Experiment 2a





Conclusions

  • The poorer performance and interaction in the Computer condition was not replicated.

  • Those effects were probably due to the stimuli sets used in the previous experiment.



Hypotheses

  • Human subjects will acquire a network’s structure more quickly if it resembles a true human social network rather than an arbitrary network.

  • Human subjects will acquire a network’s structure more quickly if it is framed as a social network as opposed to the same network framed in some other manner (e.g. a computer or transport network.)

  • Some forms of representation of the network will lead to faster acquisition than others.



Training Style







Conclusions

  • Variations in training methods seem to have little effect on graph acquisition.



Stakes Experiment

  • 135 subjects

  • 3 (Graph, within) x 3 (Stakes, between)

  • Stakes

    • Class
    • Class + You
    • Survival + You








Class



Class + You



Survival + You









Conclusions

  • Scale-free graphs are consistently the easiest to learn.

  • Acquisition does not seem to be obviously affected by framing.







Conclusions

  • Subjects acquire personally relevant information more readily.

  • Graph learning can be directed so that it is node-specific.





Centrality



Popularity Position Effect – Scale Free



Popularity Position Effect – Scale Free



Popularity Position Effect – Random



Popularity Position Effect – Random



Conclusions

  • The connection patterns of low-centrality and high-centrality nodes are acquired more readily nodes of average centrality.



What makes a graph easier to learn?



What makes a graph easier to learn?



What makes a graph easier to learn?



Graph Entropy

  • Consensus has not yet formed regarding how to quantify the entropy or information content of a graph.



Graph Entropy

  • Network Entropy Based on Topology Configuration and Its Computation to Random Networks B.H. Wang, W.X. Wang and T. Zhou



Graph Entropy

  • Information Theory of Complex Networks: On Evolution and Architectural Constraints R.V. Sole and S. Valverde (2004)



Graph Entropy

  • Gini Coefficient

  • 0 = Perfectly equal division of edges

  • 1 = Top 1 node has all edges



Prediction

  • Graph structures in which all nodes have equal degree should be especially difficult to acquire.



Watts Small World Experiment

  • 63 subjects

  • 2 (Graph, within) x 2 (Training, within)

  • Graph

    • Ring Lattice
    • Watts-Strogatz Small World
  • Training

    • Diagram
    • Edges






Training









Conclusions

  • The structure of a regular ring lattice is easier to acquire than a rewired small-world lattice.

  • At least in this instance, a diagram promotes faster acquisition.



Hypotheses

  • Human subjects will acquire a network’s structure more quickly if it resembles a true human social network rather than an arbitrary network.

  • Human subjects will acquire a network’s structure more quickly if it is framed as a social network as opposed to the same network framed in some other manner (e.g. a computer or transport network.)

  • Some forms of representation of the network will lead to faster acquisition than others.



Hypotheses

  • Human subjects will acquire a network’s structure more quickly if it resembles a true human social network rather than an arbitrary network.

  • Human subjects will acquire a network’s structure more quickly if it is framed as a social network as opposed to the same network framed in some other manner (e.g. a computer or transport network.)

  • Some forms of representation of the network will lead to faster acquisition than others.



Hypotheses

  • Human subjects will acquire a network’s structure more quickly if it resembles a true human social network rather than an arbitrary network.

  • Human subjects will acquire a network’s structure more quickly if it is framed as a social network as opposed to the same network framed in some other manner (e.g. a computer or transport network.)

  • Some forms of representation of the network will lead to faster acquisition than others.



New Experiments Running

  • Training out yes bias

  • Facebook predictors of learning rate

  • Simultaneous networks










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