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Title: Making social networks more human: A topological approach

Abstract

A key problem in social network analysis is to identify nonhuman interactions. State-of-the-art bot-detection systems like Botometer train machine-learning models on user-specific data. Unfortunately, these methods do not work on data sets in which only topological information is available. In this paper, we propose a new, purely topological approach. Our method removes edges that connect nodes exhibiting strong evidence of non-human activity from publicly available electronic-social-network datasets, including, for example, those in the Stanford Network Analysis Project repository (SNAP). Our methodology is inspired by classic work in evolutionary psychology by Dunbar that posits upper bounds on the total strength of the set of social connections in which a single human can be engaged. We model edge strength with Easley and Kleinberg's topological estimate; label nodes as “violators” if the sum of these edge strengths exceeds a Dunbar-inspired bound; and then remove the violator-to-violator edges. We run our algorithm on multiple social networks and show that our Dunbar-inspired bound appears to hold for social networks, but not for nonsocial networks. Our cleaning process classifies 0.04% of the nodes of the Twitter-2010 followers graph as violators, and we find that more than 80% of these violator nodes have Botometer scores of 0.5more » or greater. Furthermore, after we remove the roughly 15 million violator-violator edges from the 1.2-billion-edge Twitter-2010 follower graph, 34% of the violator nodes experience a factor-of-two decrease in PageRank. PageRank is a key component of many graph algorithms such as node/edge ranking and graph sparsification. Thus, this artificial inflation would bias algorithmic output, and result in some incorrect decisions based on this output.« less

Authors:
ORCiD logo [1];  [1];  [2]
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  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Univ. of New Mexico, Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1559509
Report Number(s):
SAND2019-9300J
Journal ID: ISSN 1932-1864; 678306
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Name: Statistical Analysis and Data Mining; Journal ID: ISSN 1932-1864
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Berry, Jonathan W., Phillips, Cynthia A., and Saia, Jared. Making social networks more human: A topological approach. United States: N. p., 2019. Web. doi:10.1002/sam.11420.
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Berry, Jonathan W., Phillips, Cynthia A., & Saia, Jared. Making social networks more human: A topological approach. United States. https://doi.org/10.1002/sam.11420
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Berry, Jonathan W., Phillips, Cynthia A., and Saia, Jared. Wed . "Making social networks more human: A topological approach". United States. https://doi.org/10.1002/sam.11420. https://www.osti.gov/servlets/purl/1559509.
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@article{osti_1559509,
title = {Making social networks more human: A topological approach},
author = {Berry, Jonathan W. and Phillips, Cynthia A. and Saia, Jared},
abstractNote = {A key problem in social network analysis is to identify nonhuman interactions. State-of-the-art bot-detection systems like Botometer train machine-learning models on user-specific data. Unfortunately, these methods do not work on data sets in which only topological information is available. In this paper, we propose a new, purely topological approach. Our method removes edges that connect nodes exhibiting strong evidence of non-human activity from publicly available electronic-social-network datasets, including, for example, those in the Stanford Network Analysis Project repository (SNAP). Our methodology is inspired by classic work in evolutionary psychology by Dunbar that posits upper bounds on the total strength of the set of social connections in which a single human can be engaged. We model edge strength with Easley and Kleinberg's topological estimate; label nodes as “violators” if the sum of these edge strengths exceeds a Dunbar-inspired bound; and then remove the violator-to-violator edges. We run our algorithm on multiple social networks and show that our Dunbar-inspired bound appears to hold for social networks, but not for nonsocial networks. Our cleaning process classifies 0.04% of the nodes of the Twitter-2010 followers graph as violators, and we find that more than 80% of these violator nodes have Botometer scores of 0.5 or greater. Furthermore, after we remove the roughly 15 million violator-violator edges from the 1.2-billion-edge Twitter-2010 follower graph, 34% of the violator nodes experience a factor-of-two decrease in PageRank. PageRank is a key component of many graph algorithms such as node/edge ranking and graph sparsification. Thus, this artificial inflation would bias algorithmic output, and result in some incorrect decisions based on this output.},
doi = {10.1002/sam.11420},
journal = {Statistical Analysis and Data Mining},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {7}
}
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https://doi.org/10.1002/sam.11420
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Figures / Tables:

FIGURE 1 FIGURE 1: (a) In social networks, the triadic closure property says that if vertex v has strong ties to both vertices u and w, then there is likely to be at least a weak relationship, shown as a dashed red line, between vertices u and w. (b) The clustering coeffientmore » of vertex v in a graph is the fraction of the wedges on vertex v that close into triangles. A wedge is a length-two path with v in the center, such as the path u, v, w in part (a). In part (b), vertex v has 6 wedges. There are 5 (green) closure edges, so the clustering coefficient is 5/6.« less
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