TLDR: A new method, Complementary Multi-View Neighborhood Differentiation (CMV-ND), is proposed for deep graph clustering. It tackles the challenges of large-scale graphs and missing node attributes by creating complete and non-redundant multi-view representations of graph structure. This is achieved through recursive neighborhood search and a neighborhood differential strategy. CMV-ND significantly improves the performance and scalability of existing clustering methods on attribute-missing large graphs without relying on disruptive sampling.
Deep graph clustering (DGC) is a powerful technique used to organize data points, or “nodes,” within a network (graph) into meaningful groups or “clusters.” This has wide-ranging applications, from identifying communities in social networks to making recommendations. However, real-world graphs often present two major hurdles: they are typically very large, containing millions of nodes, and they frequently have missing information, especially in the attributes or characteristics of these nodes.
Traditional DGC methods struggle with these challenges. Many are designed for smaller datasets, and when applied to large graphs, they can run into memory issues or take too long to process. Furthermore, existing methods often assume that all node attributes are complete. When attributes are missing, the performance of these methods significantly degrades. Some approaches try to fill in the missing data, but this can be difficult in very sparse, large graphs, and the common practice of “sampling” parts of the graph to make it manageable can further distort the crucial structural information.
To overcome these limitations, researchers have introduced a new method called Complementary Multi-View Neighborhood Differentiation (CMV-ND). The core idea behind CMV-ND is to process the graph’s structural information into multiple distinct “views” that are both complete and non-redundant. This approach ensures that all essential structural details are captured without unnecessary repetition, which is a common problem in traditional graph neural networks (GNNs).
CMV-ND operates in a few key steps. First, it uses a technique called Recursive Neighborhood Search (RNS). Imagine exploring a neighborhood: RNS systematically expands outwards from each node, layer by layer, to capture its local structure across different “hop” distances (how many steps away a node is). This ensures that the structural information is as complete as possible.
Next, to avoid redundancy, CMV-ND employs a Neighborhood Differential Strategy (NDS). This strategy ensures that the information gathered at each hop distance is unique and doesn’t overlap with information from closer hops. For example, if you’re looking at nodes two hops away, NDS makes sure you’re only considering those exactly two hops away, not those one hop away. This creates distinct, complementary views of the graph structure.
Finally, these K differential hop representations, along with the original features of the target node, are combined to form K+1 complementary views. These views can then be used with existing multi-view clustering or deep graph clustering methods. Unlike traditional GNNs that rely on message passing, CMV-ND directly stores graph structural information in these differential neighborhoods, allowing it to handle large-scale data without the need for disruptive sampling.
Experimental results on six widely used graph datasets, including both small and large ones like Reddit and ogbn-products, demonstrate the effectiveness of CMV-ND. When combined with existing DGC methods, CMV-ND consistently improves clustering performance, especially on large graphs with missing attributes where other methods often fail due to memory constraints. For instance, when paired with Dink-Net, CMV-ND achieved superior accuracy and F1 scores on large datasets, outperforming methods that struggled with memory limitations. The method also proved robust, maintaining performance even when attributes were complete, and was not overly sensitive to the number of views used.
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This novel paradigm not only addresses the critical challenges of large-scale and attribute-missing graphs but also bridges the gap between deep graph clustering and multi-view clustering, offering a flexible and efficient way to analyze complex network data. For more technical details, you can refer to the full research paper: Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation.
