FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks

Core Concepts

The author proposes FCS-HGNN to flexibly identify single-type and multi-type communities in HINs by dynamically considering the contribution of each relation, improving efficiency on large-scale graphs.

Abstract

Community search in heterogeneous information networks (HINs) is a personalized community discovery problem that has garnered considerable interest. Existing methods face limitations in identifying multi-type communities involving nodes of different types. The proposed FCS-HGNN method aims to address these limitations by adaptively learning community patterns and improving efficiency on large-scale graphs through neighbor sampling and depth-based heuristic search strategies.

Stats

Achieved average improvements of 14.3% and 11.1% on single-type and multi-type communities, respectively.
Utilizes a fixed number of neighbors for each node to form a subgraph for training.
Incorporates two strategies to improve efficiency: neighbor sampling algorithm and depth-based heuristic search strategy.

Quotes

"The single-type community does not fully exploit the valuable community information in HINs."
"Both metapath-based and constraint-based methods suffer from pattern inflexibility."
"Our proposed FCS-HGNN significantly enhances the effectiveness of multi-type community search."

Key Insights Distilled From

FCS-HGNN

by Guoxin Chen,... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2311.08919.pdf

Deeper Inquiries

How can FCS-HGNN be adapted for other types of networks beyond HINs

FCS-HGNN can be adapted for other types of networks beyond HINs by making some modifications to accommodate the specific characteristics of different network types. For example, in a social network where relationships are more dynamic and evolving, the edge semantic attention mechanism can be adjusted to capture temporal aspects of connections. Additionally, the type-shared feature projection can be customized to handle different types of node features present in social networks or biological networks. By tailoring these components to suit the unique attributes of diverse network structures, FCS-HGNN can effectively adapt to various network types.

What are potential drawbacks or challenges associated with the proposed neighbor sampling algorithm

One potential drawback associated with the neighbor sampling algorithm is that it may introduce bias into the training process if not implemented carefully. The fixed number of neighbors sampled at each layer could lead to an uneven representation of nodes in the subgraph, potentially skewing the learning process towards certain regions of the graph. Moreover, selecting an inappropriate fanout value could result in oversampling or undersampling certain parts of the graph, impacting the model's ability to generalize well on unseen data.

How might the depth-based heuristic strategy impact the accuracy of identifying target communities

The depth-based heuristic strategy could impact accuracy when identifying target communities by potentially missing out on nodes that are farther away but still belong to the community. If there are long-range connections within a community that extend beyond immediate neighbors, limiting exploration based solely on proximity might overlook important nodes crucial for defining a comprehensive community structure. Balancing depth constraints with thorough exploration is essential to ensure accurate identification of target communities while maintaining search efficiency.