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Ercan Engin Kuruoglu

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3 papers

2 author rows

JBHI Journal 2026 Journal Article

Learning Optimal Spectral Clustering for Functional Brain Network Generation and Classification

Jiacheng Hou
Zhenjie Song
Chenfei Ye
Ercan Engin Kuruoglu

Functional brain network (FBN) analysis aims to enhance the understanding of brain organization and support the diagnosis of neurological and psychiatric disorders. Prior studies have shown that FBNs exhibit small-world topology, where brain regions form functional clusters, and abnormalities in these clusters are strongly associated with disease. However, current learning-based methods either ignore this special topological structure or impose it as a post-hoc step outside the learning process, limiting both performance and interpretability. In this paper, we propose Learning Optimal Spectral Clustering (LOSC), a new framework that integrates the FBN generation, clustering, and classification with a novel graph theory grounded loss to fully exploit the small-world topology. Firstly, LOSC learns brain connectivity in a nonlinear spatio–spectral embedding space, guided by our proposed Rayleigh Quotient Loss (RQL), to preserve the small-world properties in generated FBNs. Then, the FBNs are partitioned into clusters of functionally synchronized regions, and both intra- and inter-cluster relations are utilized for brain network classification. Our contributions are threefold: (1) Improved brain network classification accuracy: by leveraging small-world functional clusters, LOSC achieves consistent gains of 2. 0%, 3. 6%, and 2. 6% on the ABIDE, ADHD-200, and HCP datasets compared with state-of-the-art models, respectively; (2) Theoretical grounding: with our proposed RQL, LOSC bridges the gap between the graph theory and learning-based FBN analysis; and (3) Interpretability: the discovered functional clusters align with known neuropathology and contribute to the discovery of new functional community biomarkers.

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AAAI Conference 2025 Short Paper

LLM-based Online Prediction of Time-varying Graph Signals (Student Abstract)

Dayu Qin
Yi Yan
Ercan Engin Kuruoglu

In this paper, we propose a novel framework that leverages Large Language Models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passing scheme. For each missing node, its neighbors and previous estimates are fed into and processed by LLM to infer the missing observations. Tested on the task of the online prediction of wind-speed graph signals, our model outperforms online graph filtering algorithms in terms of accuracy, demonstrating the potential of LLMs in effectively addressing partially observed signals in graphs.

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ICLR Conference 2022 Conference Paper

PAC-Bayes Information Bottleneck

Zifeng Wang 0008
Shao-Lun Huang
Ercan Engin Kuruoglu
Jimeng Sun 0001
Xi Chen 0003
Yefeng Zheng 0001

Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works trying to derive non-vacuous bounds for NNs. Recently, the compression of information stored in weights (IIW) is proved to play a key role in NNs generalization based on the PAC-Bayes theorem. However, no solution of IIW has ever been provided, which builds a barrier for further investigation of the IIW's property and its potential in practical deep learning. In this paper, we propose an algorithm for the efficient approximation of IIW. Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB. From PIB, we can empirically identify the fitting to compressing phase transition during NNs' training and the concrete connection between the IIW compression and the generalization. Besides, we verify that IIW is able to explain NNs in broad cases, e.g., varying batch sizes, over-parameterization, and noisy labels. Moreover, we propose an MCMC-based algorithm to sample from the optimal weight posterior characterized by PIB, which fulfills the potential of IIW in enhancing NNs in practice.

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