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Haoran Zheng

Publications and source records attributed to Haoran Zheng.

23 records · Page 2Linked to original sources
arXiv2024

GraSP: Simple yet Effective Graph Similarity Predictions

Graph similarity computation (GSC) is to calculate the similarity between one pair of graphs, which is a fundamental problem with fruitful applications in the graph community. In GSC, graph edit distance (GED) and maximum common subgraph (MCS) are two important similarity metrics, both of which are NP-hard to compute. Instead of calculating the exact values, recent solutions resort to leveraging graph neural networks (GNNs) to learn data-driven models for the estimation of GED and MCS. Most of them are built on components involving node-level interactions crossing graphs, which engender vast computation overhead but are of little avail in effectiveness. In the paper, we present GraSP, a simple yet effective GSC approach for GED and MCS prediction. GraSP achieves high result efficacy through several key instruments: enhanced node features via positional encoding and a GNN model augmented by a gating mechanism, residual connections, as well as multi-scale pooling. Theoretically, GraSP can surpass the 1-WL test, indicating its high expressiveness. Empirically, extensive experiments comparing GraSP against 10 competitors on multiple widely adopted benchmark datasets showcase the superiority of GraSP over prior arts in terms of both effectiveness and efficiency. The code is available at https://github.com/HaoranZ99/GraSP.

cs.LG↗
arXiv2024

Spectral Subspace Clustering for Attributed Graphs

Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domains, especially images and videos. Recently, several studies have demonstrated the great potential of subspace clustering models for partitioning vertices in attributed graphs, referred to as SCAG. However, these works either demand significant computational overhead for constructing the nxn self-expressive matrix, or fail to incorporate graph topology and attribute data into the subspace clustering framework effectively, and thus, compromise result quality. Motivated by this, this paper presents two effective and efficient algorithms, S2CAG and M-S2CAG, for SCAG computation. Particularly, S2CAG obtains superb performance through three major contributions. First, we formulate a new objective function for SCAG with a refined representation model for vertices and two non-trivial constraints. On top of that, an efficient linear-time optimization solver is developed based on our theoretically grounded problem transformation and well-thought-out adaptive strategy. We then conduct an in-depth analysis to disclose the theoretical connection of S2CAG to conductance minimization, which further inspires the design of M-S2CAG that maximizes the modularity. Our extensive experiments, comparing S2CAG and M-S2CAG against 17 competitors over 8 benchmark datasets, exhibit that our solutions outperform all baselines in terms of clustering quality measured against the ground truth while delivering high efficiency

cs.SI↗
arXiv2024

Patient-Specific CT Doses Using DL-based Image Segmentation and GPU-based Monte Carlo Calculations for 10,281 Subjects

Computed tomography (CT) scans are a major source of medical radiation exposure worldwide. In countries like China, the frequency of CT scans has grown rapidly, particularly in routine physical examinations where chest CT scans are increasingly common. Accurate estimation of organ doses is crucial for assessing radiation risk and optimizing imaging protocols. However, traditional methods face challenges due to the labor-intensive process of manual organ segmentation and the computational demands of Monte Carlo (MC) dose calculations. In this study, we present a novel method that combines automatic image segmentation with GPU-accelerated MC simulations to compute patient-specific organ doses for a large cohort of 10,281 individuals undergoing CT examinations for physical examinations at a Chinese hospital. This is the first big-data study of its kind involving such a large population for CT dosimetry. The results show considerable inter-individual variability in CTDIvol-normalized organ doses, even among subjects with similar BMI or WED. Patient-specific organ doses vary widely, ranging from 33% to 164% normalized by the doses from ICRP Adult Reference Phantoms. Statistical analyses indicate that the "Reference Man" based average phantoms can lead to significant dosimetric uncertainties, with relative errors exceeding 50% in some cases. These findings underscore the fact that previous assessments of radiation risk may be inaccurate. It took our computational tool, on average, 135 seconds per subject, using a single NVIDIA RTX 3080 GPU card. The big-data analysis provides interesting data for improving CT dosimetry and risk assessment by avoiding uncertainties that were neglected in the past.

physics.med-ph↗
arXiv2024

SCENE: Evaluating Explainable AI Techniques Using Soft Counterfactuals

Explainable Artificial Intelligence (XAI) plays a crucial role in enhancing the transparency and accountability of AI models, particularly in natural language processing (NLP) tasks. However, popular XAI methods such as LIME and SHAP have been found to be unstable and potentially misleading, underscoring the need for a standardized evaluation approach. This paper introduces SCENE (Soft Counterfactual Evaluation for Natural language Explainability), a novel evaluation method that leverages large language models (LLMs) to generate Soft Counterfactual explanations in a zero-shot manner. By focusing on token-based substitutions, SCENE creates contextually appropriate and semantically meaningful Soft Counterfactuals without extensive fine-tuning. SCENE adopts Validitysoft and Csoft metrics to assess the effectiveness of model-agnostic XAI methods in text classification tasks. Applied to CNN, RNN, and Transformer architectures, SCENE provides valuable insights into the strengths and limitations of various XAI techniques.

cs.AI↗