ProCom: A Few-shot Targeted Community Detection Algorithm

TL;DR

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Abstract

Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying fraud groups in transaction networks. Traditional community detection methods fail to capture the specific features of the targeted community and detect all types of communities indiscriminately. Semi-supervised community detection algorithms, emerged as a feasible alternative, are inherently constrained by their limited adaptability and substantial reliance on a large amount of labeled data, which demands extensive domain knowledge and manual effort. In this paper, we address the aforementioned weaknesses in targeted community detection by focusing on few-shot scenarios. We propose ProCom, a novel framework that extends the ``pre-train, prompt'' paradigm, offering a low-resource, high-efficiency, and transferable solution. Within the framework, we devise a dual-level context-aware pre-training method that fosters a deep understanding of latent communities in the network, establishing a rich knowledge foundation for downstream task. In the prompt learning stage, we reformulate the targeted community detection task into pre-training objectives, allowing the extraction of specific knowledge relevant to the targeted community to facilitate effective and efficient inference. By leveraging both the general community knowledge acquired during pre-training and the specific insights gained from the prompt communities, ProCom exhibits remarkable adaptability across different datasets. We conduct extensive experiments on five benchmarks to evaluate the ProCom framework, demonstrating its SOTA performance under few-shot scenarios, strong efficiency, and transferability across diverse datasets.

Figures (5)

• Figure 1: A subgraph of a trading network with both normal and fraud communities. (a) Traditional community detection tends to identify both kinds of communities. (b) Semi-supervised community detection may pinpoint the remaining fraud community but requires a substantial amount of labeled data. (c) ProCom applies the "pre-train, prompt" paradigm to tackle the task under few-shot settings, typical in low-resource learning, efficient and transferable inference.
• Figure 2: Overview of ProCom. During the pre-training stage, we devise a dual-level pre-training method to guide the model in understanding the latent communities in the network. In the subsequent prompt learning stage, aided with few-shot samples, we reformulate the targeted community detection task into pretexts, facilitating prediction in a parameter-efficient manner.
• Figure 3: Prompt sensitivity study on varying numbers of prompts (training samples for semi-supervised methods).
• Figure 4: Ablation study on the effectiveness of both pretexts and prompt learning within the ProCom framework.
• Figure 5: Case study of how prompting function works.