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Modeling the Functional Network for Spatial Navigation in the Human Brain

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An Empirical Evaluation of Network Representation Learning Methods.

Alexandru Cristian Mara¹, Jefrey Lijffijt¹, Tijl De Bie¹

¹Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.

|March 10, 2022

Summary

This summary is machine-generated.

Network representation learning (NRL) methods embed network nodes into vectors. Despite complex evaluations, recent NRL progress is limited, with many methods failing to outperform simple heuristics.

Keywords:

benchmark evaluation link prediction network embedding network reconstruction representation learning

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Area of Science:

Computer Science
Data Science
Network Analysis

Background:

Network representation learning (NRL) methods create vector embeddings for network nodes.
These embeddings aim to preserve network properties for downstream tasks like link prediction.
Current evaluation practices for NRL are complex and lack standardization, hindering progress assessment.

Purpose of the Study:

To investigate the impact of various design choices on NRL method performance.
To provide an extensive and consistent evaluation of state-of-the-art NRL approaches.
To clarify the actual progress in the field of network representation learning.

Main Methods:

Systematic evaluation of diverse NRL design choices.
Consistent benchmarking across multiple downstream tasks.
Comparison of embedding-based methods against heuristic baselines.

Main Results:

Limited empirical progress observed in recent years for NRL.
Embedding-based NRL methods often underperform simple heuristics.
Evaluation pipeline complexity and design choices significantly impact reported performance.

Conclusions:

The field of network representation learning requires more standardized evaluation.
Current NRL methods may not offer substantial advantages over basic heuristics in many practical scenarios.
Further research is needed to develop more robust and effective NRL techniques.