Author name cluster

Jianjun Yu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

TMLR Journal 2025 Journal Article

Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement

Wenjing Chang
Kay Liu
Philip S. Yu
Jianjun Yu

Graph anomaly detection (GAD) is becoming increasingly crucial in various applications, ranging from financial fraud detection to fake news detection. However, current GAD methods largely overlook the fairness problem, which might result in discriminatory decisions skewed toward certain demographic groups defined on sensitive attributes (e.g., gender). This greatly limits the applicability of these methods in real-world scenarios in light of societal and ethical restrictions. To address this critical gap, we make the first attempt to integrate fairness with utility in GAD decision-making. Specifically, we devise a novel DisEntangle-based FairnEss-aware aNomaly Detection framework on the attributed graph, named DEFEND. DEFEND first introduces disentanglement in GNNs to capture informative yet sensitive-irrelevant node representations, effectively reducing bias inherent in graphrepresentation learning. Besides, to alleviate discriminatory bias in evaluating anomalies, DEFEND adopts a reconstruction-based method, which concentrates solely on node attributes and avoids incorporating biased graph topology. Additionally, given the inherent association between sensitive-relevant and -irrelevant attributes, DEFEND further constrains the correlation between the reconstruction error and predicted sensitive attributes. Empirical evaluations on real-world datasets reveal that DEFEND performs effectively in GAD and significantly enhances fairness compared to state-of-the-art baselines. Our code is available at https://github.com/AhaChang/DEFEND.

PDF Details

EAAI Journal 2025 Journal Article

EnTAIL: Evolutional temporal-aware interaction learning for motion forecasting

Chunyu Liu
Hao Dong
Pengyang Wang
Jianjun Yu

Accurately predicting the future trajectories of traffic agents in real-world scenarios is critical for advancing intelligent cyber–physical systems (CPS), such as autonomous driving systems and smart cities. A fundamental challenge lies in mining the evolving interaction patterns among multiple agents from their past trajectories, as traffic scenarios often exhibit complex interactions and continuously evolve along the timeline. However, existing methods fail to fully exploit the temporality inherent in sequential interactions. In the process of modeling interactions, they lack a comprehensive understanding of static interactions that occur at constant timestamps and the evolving patterns of interactions across timestamps. To tackle these challenges, we propose Evolutional Temporal-Aware Interaction Learning (EnTAIL), a novel temporal-aware interaction learning framework to model and reason the interactions among agents. EnTAIL captures both static interaction patterns at individual timestamps and temporal-aware interaction patterns across timestamps through a unified framework. Specifically, we introduce a trainable constant time encoding to integrate with the interaction modeling in each individual timestamp, which aims to capture the static interaction information. We propose a dynamic evolution encoder to model temporal-aware interaction features, enabling learning both short-term and long-term interactions within multiscaled observation windows. Besides, EnTAIL also considers the temporal feature in the prediction stage and models the long-range interactions ignored during the encoding phase. Extensive experiments conducted on the challenging real-world Argoverse dataset demonstrate that our proposed model achieves substantial performance improvement and outperforms the baseline methods up to 2. 5% in minimum Average Displacement Error (minADE) and 1. 2% in minimum Final Displacement Error (minFDE).

Details DOI

AAAI Conference 2010 Conference Paper

Fast Algorithms for Top-k Approximate String Matching

Zhenglu Yang
Jianjun Yu
Masaru Kitsuregawa

Top-k approximate querying on string collections is an important data analysis tool for many applications, and it has been exhaustively studied. However, the scale of the problem has increased dramatically because of the prevalence of the Web. In this paper, we aim to explore the efficient top-k similar string matching problem. Several efficient strategies are introduced, such as length aware and adaptive q-gram selection. We present a general q-gram based framework and propose two efficient algorithms based on the strategies introduced. Our techniques are experimentally evaluated on three real data sets and show a superior performance.

PDF Details