Global vs Local Social Search in the Formation of Coalitions

1997 ◽  
pp. 203-209
Author(s):  
Giuliano Pistolesi ◽  
Vito Veneziano ◽  
Cristiano Castelfranchi
Keyword(s):  
Social Search

Social Search

2012 ◽  
Author(s):  
Manuel Mueller-Frank ◽  
Mallesh Pai
Keyword(s):  
Social Search

A Comparison of Machine Learning Techniques for Recommending Search Experiences in Social Search

2012 ◽  
pp. 195-200 ◽  
Author(s):  
Zurina Saaya ◽  
Markus Schaal ◽  
Maurice Coyle ◽  
Peter Briggs ◽  
Barry Smyth
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Social Search ◽  
Learning Techniques

scholarly journals A partial knowledge of friends of friends speeds social search

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255982
Author(s):  
Amr Elsisy ◽  
Boleslaw K. Szymanski ◽  
Jasmine A. Plum ◽  
Miao Qi ◽  
Alex Pentland
Keyword(s):  
Social Network ◽  
Human Mobility ◽  
Sufficient Conditions ◽  
Partial Knowledge ◽  
Social Search ◽  
Strongly Nonlinear ◽  
Original Distribution ◽  
Personal Connections ◽  
Location Based Social Network ◽  
Necessary And Sufficient

Milgram empirically showed that people knowing only connections to their friends could locate any person in the U.S. in a few steps. Later research showed that social network topology enables a node aware of its full routing to find an arbitrary target in even fewer steps. Yet, the success of people in forwarding efficiently knowing only personal connections is still not fully explained. To study this problem, we emulate it on a real location-based social network, Gowalla. It provides explicit information about friends and temporal locations of each user useful for studies of human mobility. Here, we use it to conduct a massive computational experiment to establish new necessary and sufficient conditions for achieving social search efficiency. The results demonstrate that only the distribution of friendship edges and the partial knowledge of friends of friends are essential and sufficient for the efficiency of social search. Surprisingly, the efficiency of the search using the original distribution of friendship edges is not dependent on how the nodes are distributed into space. Moreover, the effect of using a limited knowledge that each node possesses about friends of its friends is strongly nonlinear. We show that gains of such use grow statistically significantly only when this knowledge is limited to a small fraction of friends of friends.

scholarly journals Ecology of social search for learning resources

2009 ◽  
Vol 26 (4) ◽  
pp. 272-286 ◽  
Author(s):  
Riina Vuorikari ◽  
Rob Koper
Keyword(s):  
Learning Resources ◽  
Social Search

scholarly journals Social Search

2018 ◽  
pp. 213-276 ◽  
Author(s):  
Peter Brusilovsky ◽  
Barry Smyth ◽  
Bracha Shapira
Keyword(s):  
Social Search
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