  Graph anomaly detection (GAD) has gained increasing attention in recent years
due to its critical application in a wide range of domains, such as social
networks, financial risk management, and traffic analysis. Existing GAD methods
can be categorized into node and edge anomaly detection models based on the
type of graph objects being detected. However, these methods typically treat
node and edge anomalies as separate tasks, overlooking their associations and
frequent co-occurrences in real-world graphs. As a result, they fail to
leverage the complementary information provided by node and edge anomalies for
mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and
SL-GAD, heavily rely on negative pair sampling in contrastive learning, which
incurs high computational costs, hindering their scalability to large graphs.
To address these limitations, we propose a novel unified graph anomaly
detection framework based on bootstrapped self-supervised learning (named
BOURNE). We extract a subgraph (graph view) centered on each target node as
node context and transform it into a dual hypergraph (hypergraph view) as edge
context. These views are encoded using graph and hypergraph neural networks to
capture the representations of nodes, edges, and their associated contexts. By
swapping the context embeddings between nodes and edges and measuring the
agreement in the embedding space, we enable the mutual detection of node and
edge anomalies. Furthermore, BOURNE can eliminate the need for negative
sampling, thereby enhancing its efficiency in handling large graphs. Extensive
experiments conducted on six benchmark datasets demonstrate the superior
effectiveness and efficiency of BOURNE in detecting both node and edge
anomalies.
