Exploring Unknown Social Networks for Discovering Hidden Nodes

TL;DR

This paper presents a machine learning-based graph exploration framework for discovering hidden nodes in unknown social networks, demonstrating near-optimal query costs and analyzing the impact of node embeddings with bandit algorithms.

Contribution

It introduces a novel graph exploration approach for hidden node discovery, incorporating node embeddings and bandit algorithms to improve efficiency in unknown social graphs.

Findings

Graph exploration strategies achieve near-known topology efficiency.

Using node embeddings can both help and hinder discovery efficiency.

Bandit algorithms effectively combine models for diverse scenarios.

Abstract

In this paper, we address the challenge of discovering hidden nodes in unknown social networks, formulating three types of hidden-node discovery problems, namely, Sybil-node discovery, peripheral-node discovery, and influencer discovery. We tackle these problems by employing a graph exploration framework grounded in machine learning. Leveraging the structure of the subgraph gradually obtained from graph exploration, we construct prediction models to identify target hidden nodes in unknown social graphs. Through empirical investigations of real social graphs, we investigate the efficiency of graph exploration strategies in uncovering hidden nodes. Our results show that our graph exploration strategies discover hidden nodes with an efficiency comparable to that when the graph structure is known. Specifically, the query cost of discovering 10% of the hidden nodes is at most only 1.2 times that when the topology is known, and the query-cost multiplier for discovering 90% of the hidden nodes is at most only 1.4. Furthermore, our results suggest that using node embeddings, which are low-dimensional vector representations of nodes, for hidden-node discovery is a double-edged sword: it is effective in certain scenarios but sometimes degrades the efficiency of node discovery. Guided by this observation, we examine the effectiveness of using a bandit algorithm to combine the prediction models that use node embeddings with those that do not, and our analysis shows that the bandit-based graph exploration strategy achieves efficient node discovery across a wide array of settings.