An Attention-based Collaboration Framework for Multi-View Network Representation Learning (1709.06636v1)

Abstract: Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.

An Attention-Based Collaboration Framework for Multi-View Network Representation Learning

The paper presented introduces a novel framework designed to learn node representations in multi-view networks by effectively leveraging multiple types of proximities that exist in real-world network data. This work is pivotal in advancing the current methodologies that often operate under the constraint of single-view network assumptions, thereby expanding the capacity to accommodate the complexity of modern datasets.

Framework and Methodology:

The proposed approach is structured around a collaboration framework that integrates an attention mechanism to dynamically weight the significance of different views, thus enabling a more robust and comprehensive representation of network nodes. The framework employs view-specific node representations to capture individual view proximities. Subsequently, it introduces a novel voting mechanism where these representations converge to form a unified, robust node representation.

The attention mechanism allows the model to differentially focus on views based on their relevance and quality, implicitly across nodes, through learned attention weights determined in part by a small set of labeled data. This is calculated using a softmax function over concatenated view-specific features, thereby aligning with nodes' informativeness in given tasks. This is a marked improvement over existing methods which often assume equal importance for all views, regardless of their inherent disparities.

Experimental Analysis and Results:

The experimental validation implemented in this paper involved a broad array of datasets for applications such as node classification and link prediction. Quantitative evaluations using well-established metrics like Macro-F1, Micro-F1 for classification, and AUC for link prediction reveal that the proposed method surpasses traditional single-view and some multi-view baselines. Specifically, the attention-mechanism enhanced variants outperformed versions with static view-weighting, underpinning the benefit of a dynamically weighted, multi-view approach.

The evaluation extended to the robustness of node representations under data sparsity scenarios, indicating the method's effectiveness in sparse data conditions—a significant challenge in network analysis. The attentiveness to view quality, enabled by the attention mechanism, ensures the derivation of semantically rich and informative embeddings even when individual views are sparse or noisy.

Implications and Future Work:

The implications of this work are substantial in the field of network representation learning. The attention framework equips models with the capability to adaptively and efficiently synthesize information from diverse and potentially heterogeneous data structures, an essential feature given the growing scale and complexity of network datasets. Theoretical underpinnings in graph theory and data representation learning are simultaneously expanded by introducing a framework that is broadly applicable across multiple domains, including social networks, biological interactions, and citation analysis.

The future scope suggested by the authors includes the extension of their framework to heterogeneous information networks. By assessing multiple node types and complex relational structures, this approach could address meta-path proximities in more nuanced ways. Furthermore, harnessing increased computational capacities and deploying the model in dynamically evolving networks could also yield promising results, potentially embedding temporal aspects into the multi-view learning paradigm.

In essence, this paper provides a comprehensive methodology for advancing multi-view network embedding practices by aligning robust mathematical frameworks with practical attention-based methods, highlighting substantial improvements and holding promise for future network analysis innovations.