[Paper Review] Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks

Shobeir Fakhraei, Dhanya Sridhar+2 more|arXiv (Cornell University)|Jul 2, 2016

Advanced Graph Neural Networks9 references3 citations

TL;DR

This paper proposes a dynamic, model-aware framework for constructing multi-relational neighborhood graphs during inference, adapting graph structure based on intermediate inference states and relation characteristics. By integrating nomination and activation components that prioritize key instances and relations, the method improves link prediction performance—demonstrated on drug-target interaction data—while reducing reliance on exhaustive similarity computation and enhancing scalability and accuracy over static graph construction approaches.

ABSTRACT

A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction. Most methods employ a sequential process to construct the neighborhood graph. This process often consists of generating a candidate graph, pruning the candidate graph to make a neighborhood graph, and then performing inference on the variables (i.e., nodes) in the neighborhood graph. In this paper, we propose a framework that can dynamically adapt the neighborhood graph based on the states of variables from intermediate inference results, as well as structural properties of the relations connecting them. A key strength of our framework is its ability to handle multi-relational data and employ varying amounts of relations for each instance based on the intermediate inference results. We formulate the link prediction task as inference on neighborhood graphs, and include preliminary results illustrating the effects of different strategies in our proposed framework.

Motivation & Objective

• To address the limitations of sequential, model-agnostic neighborhood graph construction in multi-relational data.
• To develop a framework that dynamically adapts the neighborhood graph during inference using intermediate results and relation structure.
• To improve link prediction performance by incorporating model uncertainty and decision boundaries into graph construction.
• To reduce computational cost by focusing on high-impact relations and instances through adaptive selection.
• To enable scalable, effective inference in large, multi-relational networks by unifying graph construction and inference.

Proposed method

• The framework dynamically constructs neighborhood graphs by interleaving inference and graph adaptation, using intermediate variable states.
• It introduces a nomination component that selects instances for additional relation inclusion based on uncertainty or importance, guided by a quota.
• An activation component ranks relations by their predictive relevance using activation scores derived from model states.
• The method combines nomination and activation to guide the selection of relations, prioritizing those most informative for inference.
• It formulates link prediction as inference on adaptive neighborhood graphs, with relation weights learned from the model.
• The framework supports multi-relational data by integrating diverse relation types (e.g., sequence, PPI, GO) and learning their relative importance.

Experimental results

Research questions

• RQ1 How can neighborhood graph construction be made adaptive to intermediate inference states in multi-relational networks?
• RQ2 What is the impact of model-aware graph construction on link prediction performance compared to static, sequential methods?
• RQ3 Can dynamic selection of instances and relations improve scalability and predictive accuracy in large networks?
• RQ4 How do nomination and activation components compare in guiding graph evolution during inference?
• RQ5 To what extent can multi-relational signals be effectively combined in a dynamic graph construction framework?

Key findings

• The AWL nomination method with a 10% quota improved AUPR performance over a baseline that selected k relations without constraints.
• The activation-only method achieved higher AUPR than the nomination-only method, even with fewer selected similarities, indicating the effectiveness of score-based relation prioritization.
• Combining nomination and activation components yielded higher performance with fewer selected similarities in early iterations, suggesting efficient exploration of the search space.
• The number of selected similarities was consistently lower in the proposed methods compared to the baseline, demonstrating reduced computational overhead.
• The framework effectively leveraged diverse relation types—sequence, PPI, and Gene Ontology—improving link prediction in a drug-target interaction dataset.
• The results show that model-aware, dynamic graph construction can outperform static, sequential approaches in both accuracy and efficiency.