Provable Filter for Real-world Graph Clustering: A Novel Approach with Theoretical Support

Concetti Chiave

The author presents a novel solution for real-world graph clustering, addressing the limitations of existing methods by incorporating homophilic and heterophilic edges. By leveraging neighbor information, the proposed method outperforms state-of-the-art clustering techniques.

Sintesi

The content introduces a novel approach to graph clustering that considers both homophilic and heterophilic graphs. By restructuring graphs and applying adaptive filters, the method demonstrates superior performance in practical scenarios. Experimental results validate the effectiveness of the proposed technique across various datasets.

Existing methods face challenges with homophily assumptions in graphs, leading to limited applicability in real-world scenarios.
The proposed Provable Filter for Graph Clustering (PFGC) method overcomes these limitations by capturing both low- and high-frequency information.
Through theoretical analysis and empirical experiments, PFGC showcases significant improvements in clustering accuracy compared to traditional methods.
Parameter analysis reveals the importance of integrating high-frequency information in heterophilic graphs while balancing low- and high-frequency components in homophilic graphs.

Statistiche

Most edges can be correctly distinguished by neighbor information.
Heterophilic edges can be identified with high precision.
Homophily Ratio: 0.8137 - 0.8272 for different datasets.

Citazioni

"Most edges can be correctly distinguished by neighbor information."
"Heterophilic edges can be identified with high precision."

Approfondimenti chiave tratti da

Provable Filter for Real-world Graph Clustering

by Xuanting Xie... alle arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03666.pdf

Domande più approfondite

How does the incorporation of global and local filters enhance the discriminativeness of clusters

The incorporation of global and local filters enhances the discriminativeness of clusters by capturing different levels of information from the graph. Global filters, which have a larger receptive field, can capture low-frequency components that are associated with homophily in graphs. These filters help identify nodes that are likely to belong to the same cluster based on their similarities in attributes or topology. On the other hand, local filters focus on high-frequency components that represent heterophilic relationships between nodes. By combining both types of filters in an adaptive manner, the model can effectively distinguish between nodes belonging to different clusters based on their varying degrees of similarity or dissimilarity.

What implications does the balance between low- and high-frequency components have on graph clustering performance

The balance between low- and high-frequency components plays a crucial role in graph clustering performance. Low-frequency components captured by global filters tend to highlight homophilic relationships where connected nodes share common characteristics or attributes. On the other hand, high-frequency components captured by local filters emphasize heterophilic relationships where connected nodes exhibit differences or dissimilarities. Balancing these two types of information allows for a more comprehensive understanding of the underlying structure of the graph data, leading to improved clustering accuracy and discriminativeness.

How might considering structural disparity impact other areas of graph analysis beyond clustering

Considering structural disparity in graph analysis beyond clustering can have significant implications for various applications. In network analysis, understanding both homophilic and heterophilic connections can provide insights into community detection, anomaly detection, link prediction, and network visualization tasks. For example:

Community Detection: Identifying communities within networks becomes more accurate when considering both similar (homophilous) and dissimilar (heterophilous) connections.
Anomaly Detection: Anomalies may manifest as outliers in either homophilic or heterophilic structures within a network.
Link Prediction: Predicting future links accurately requires an understanding of how both similar and dissimilar pairs interact over time.
Network Visualization: Visual representations should reflect not only clustered groups but also outlier connections that deviate from typical patterns.
By incorporating structural disparity into various aspects of graph analysis beyond clustering, researchers and practitioners can gain deeper insights into complex network behaviors across diverse domains such as social networks, biological networks, transportation systems, etc.