Summary
Traditional graph anomaly detection focus on single type of graph object (e.g., node, edge, graph). To address this, this paper introduces the first unified framework (UniGAD) for detecting anomalies at the node, edge, and graph levels jointly. The authors propose two core modules, MRQSampler and GraphStitch, to address the challenge of unifying multi-level task formats and unifying multi-level task training, respectively. Moreover, the authors theoretically prove that MRQSampler maximizes the accumulated spectral energy of subgraphs (i.e., the Rayleigh quotient) to preserve the most significant anomaly information. Extensive experiments demonstrate that UniGAD surpasses existing single-task GAD methods and graph prompt-based approaches for multiple tasks, offering robust zero-shot task transferability.
Strengths
Originality:
1. This paper focus on the gap of multi-level graph neural network at the node, edge, and graph levels, which is an important problem. In many scenarios/data, the labels of these different graph objects are often relevant, but this is always overlooked.
2. The problem formulation in Definition 2.1 is novel in graph learning, which is feasible to have labels at one or more levels, which makes the framework broadly applicable and provides strong zero-shot capabilities.
3. The authors designed a theoretically guaranteed graph sampling algorithm that uses the Maximum Rayleigh Quotient to sample the most anomalous subgraphs. This approach ensures sampling nodes that contain anomalous information, preventing the anomalous information from being smoothed out.
4. The authors implement multi-level task information transfer through a module called GraphStitch. Since multi-task in graph learning research remains rare, this network design is very interesting and prospective.
Quality:
The paper exhibits a high level of technical quality, with theoretical proofs and rigorous dynamic programming algorithms provided for the effectiveness of the MRQSampler in maximizing the accumulated spectral energy of subgraphs.
The paper proposes a robust framework that achieves the unified multi-level tasks through a pre-trained GNN encoder, MRQsampler, and GraphStitch network.
The experiments look solid, including 13 datasets with single-graph datasets and multi-graph datasets and 17 SOTA model with node-level models, edge-level models, graph-level models and multi-task models.
Clarity:
The paper is well-written and clearly presents its contributions.
Significance:
The framework of UniGAD further improves the effect of graph anomaly detection and has strong zero-shot capability. Besides, it also promote the development of multi-task learning in the graph learning domain.
Weaknesses
A substantive assessment of the weaknesses of the paper. Focus on constructive and actionable insights on how the work could improve towards its stated goals. Be specific, and avoid generic remarks. For example, if you believe the contribution lacks novelty, provide references and an explanation as evidence; if you believe experiments are insufficient, explain why and exactly what is missing, etc. Please keep in mind that the rebuttal period is not necessarily enough to run new experiments. As a result, asking for new results is rather unrealistic.
Overall, this paper is technically solid and novel. In Page 8, the authors mention that the other multi-task method, like GraphPrompt and All-in-One, often run out of time (OOT) and calculate redundantly. What is it that makes UniGAD avoid this? Are each node’s representations calculated only once? Please explain the difference in more detail.
Minors: in Additional Experimental Results, the caption of the table 8 and table 9 should be F1-macro? Otherwise this will repeat table 1,2.
Questions
Refer to weaknesses, please explain the difference in more detail and check the typos.
Limitations
The authors addressed limitations and societal impacts in the conclusion and appendices.