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.
HOVER: Homophilic Oversampling via Edge Removal for Class-Imbalanced Bot Detection on Graphs
As malicious bots reside in a network to disrupt network stability, graph neural networks (GNNs) have emerged as one of the most popular bot detection methods. However, in most cases these graphs are significantly class-imbalanced. To address this issue, graph oversampling has recently been proposed to synthesize nodes and edges, which still suffers from graph heterophily, leading to suboptimal performance. In this paper, we propose HOVER, which implements Homophilic Oversampling Via Edge Removal for bot detection on graphs. Instead of oversampling nodes and edges within initial graph structure, HOVER designs a simple edge removal method with heuristic criteria to mitigate heterophily and learn distinguishable node embeddings, which are then used to oversample minority bots to generate a balanced class distribution without edge synthesis. Experiments on TON IoT networks demonstrate the state-of-the-art performance of HOVER on bot detection with high graph heterophily and extreme class imbalance.
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- Award ID(s):
- 2245968
- PAR ID:
- 10476180
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM International Conference on Information and Knowledge Management
- ISBN:
- 9798400701245
- Page Range / eLocation ID:
- 3728-3732
- Subject(s) / Keyword(s):
- Bot detection Graph convolutional networks Imbalanced classes Homophily and heterophily
- Format(s):
- Medium: X
- Location:
- Birmingham United Kingdom
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3583780.3615264
