Unsupervised Attributed Multiplex Network Embedding (1911.06750v2)

Abstract: Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes. Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph. We devise a systematic way to jointly integrate the node embeddings from multiple graphs by introducing 1) the consensus regularization framework that minimizes the disagreements among the relation-type specific node embeddings, and 2) the universal discriminator that discriminates true samples regardless of the relation types. We also show that the attention mechanism infers the importance of each relation type, and thus can be useful for filtering unnecessary relation types as a preprocessing step. Extensive experiments on various downstream tasks demonstrate that DMGI outperforms the state-of-the-art methods, even though DMGI is fully unsupervised.

• The paper introduces DMGI, an unsupervised method that learns node embeddings by integrating various relation types and node attributes through a consensus regularization framework.
• It leverages an attention mechanism within a GCN structure to focus on significant relationships, improving performance in clustering, similarity search, and classification tasks.
• Experimental results on datasets like ACM and Amazon demonstrate that DMGI outperforms traditional methods, paving the way for advanced graph representation learning.

Unsupervised Attributed Multiplex Network Embedding Essay

The paper "Unsupervised Attributed Multiplex Network Embedding" by Chanyoung Park et al. presents an innovative method for learning node representations in multiplex networks, where nodes can be interconnected by multiple types of relations and accompanied by node attributes. Traditional approaches inadequately address these complexities as they usually assume a single type of relationship between nodes, neglect node attributes, or require supervision through node labels. The authors propose a method named Deep Multiplex Graph Infomax (DMGI) that bridges these gaps by maximizing mutual information between local-node features and a global graph representation without supervision.

Methodology

DMGI is formulated upon the principles of Deep Graph Infomax (DGI), which promotes the learning of node embeddings by maximizing mutual information between the local neighborhood and global graph representation. The novel aspect DMGI introduces is the systematic handling of multiple interrelated graphs that characterizes the multiplex network. The approach addresses several key issues:

1. Multiplex Network Structure: DMGI incorporates multiple types of relationships in the network by employing a consensus regularization framework. This framework minimizes discrepancies between differently typed node embeddings, ensuring consistency in the learned representations.
2. Attribute Utilization: DMGI naturally integrates node attributes into the embedding process through Graph Convolutional Networks (GCNs), allowing for an enriched representation learning that reflects attribute-linked structures in the network.
3. Unsupervised Learning: Unlike many contemporary methods, DMGI operates without relying on node labels, leveraging unsupervised training which significantly broadens its applicability.
4. Attention Mechanism: The method provides a mechanism for gauging the importance of each type of relation, facilitating a focused embedding process that can discard irrelevant relationships to enhance efficiency.

Experimental Results

The findings from the experiments demonstrate substantial improvement over state-of-the-art techniques in various tasks. Specifically, DMGI outperforms previous methodologies in node clustering, similarity search, and node classification despite its unsupervised setting.

• Node Clustering and Similarity Search: The effectiveness of DMGI is evident in the clustering of nodes and similarity-based tasks, where it surpasses existing methods across datasets like ACM, IMDB, DBLP, and Amazon. For instance, in similarity search tasks, DMGI shows a significant increase in the similarity ratio (Sim@5) when compared to traditional methods that separately handle relations and attributes.
• Node Classification: In supervised tasks, DMGI also demonstrates superior performance, indicating that even in the absence of labels, the embeddings obtained are highly informative. This performance is particularly notable in datasets with a rich structure-property relation such as ACM and Amazon, where multiplex networks inherently present due to diverse relation types.

Implications and Future Work

The implications of DMGI extend across both practical applications and theoretical developments:

• Practical Applications: Practically, DMGI facilitates the analysis of large-scale networks such as social media platforms, citation networks, or biological networks where multiple interactions exist, requiring a nuanced understanding of both structure and content.
• Theoretical Developments: The multiplexity management and seamless integration of node attributes introduce promising directions in graph representation learning. Future work could explore scalability to even larger networks and real-time embedding updates as network data evolves.
• Attention and Filtering: The attention mechanism deployed offers a utility in preprocessing, potentially guiding information filtering in multi-layered contexts where computational resources must be judiciously managed.

DMGI presents a compelling progression in network embedding methodologies by addressing multiplexity and attributes within an unsupervised model, setting a pathway for future explorations in more complex and naturally occurring network structures.