27 citations · 103 across the 13 of their papers we have counts for
18 papers · 1 filter
Descriptive Kernel Convolution Network with Improved Random Walk Kernel
Meng-Chieh Lee, Lingxiao Zhao, Leman Akoglu
Graph kernels used to be the dominant approach to feature engineering for structured data, which are superseded by modern GNNs as the former lacks learnability. Recently, a suite o…
Unified Discrete Diffusion for Categorical Data
Lingxiao Zhao, Xueying Ding, Lijun Yu +1
Discrete diffusion models have seen a surge of attention with applications on naturally discrete data such as language and graphs. Although discrete-time discrete diffusion has bee…
Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
Lingxiao Zhao, Xueying Ding, Leman Akoglu
Graph generation has been dominated by autoregressive models due to their simplicity and effectiveness, despite their sensitivity to ordering. Yet diffusion models have garnered in…
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach
Konstantinos Sotiropoulos, Lingxiao Zhao, Pierre Jinghong Liang +1
Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances? Many real-world pro…
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
Jaemin Yoo, Yue Zhao, Lingxiao Zhao +1
Self-supervised learning (SSL) has proven effective in solving various problems by generating internal supervisory signals. Unsupervised anomaly detection, which faces the high cos…
Self-Tuning Self-Supervised Image Anomaly Detection
Jaemin Yoo, Lingxiao Zhao, Leman Akoglu
Self-supervised learning (SSL) has emerged as a promising paradigm that presents supervisory signals to real-world problems, bypassing the extensive cost of manual labeling. Conseq…