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Arlei Silva

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6 papers

1 author row

AAAI Conference 2026 Conference Paper

Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity

Joao Mattos
Debolina Halder Lina
Arlei Silva

Link prediction is a fundamental task in graph machine learning with applications ranging from social recommendation to knowledge graph completion. Fairness in this setting is critical, as biased predictions can exacerbate societal inequalities. Prior work adopts a dyadic definition of fairness, enforcing fairness through demographic parity between intra-group and inter-group link predictions. However, we show that this dyadic framing can obscure underlying disparities across subgroups, allowing systemic biases to go undetected. Moreover, we argue that demographic parity does not meet the desired properties for fairness assessment in ranking-based tasks such as link prediction. We formalize the limitations of existing fairness evaluations and propose a framework that enables a more expressive assessment. Additionally, we propose a lightweight post-processing method combined with decoupled link predictors that effectively mitigates bias and achieves state-of-the-art fairness–utility trade-offs.

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TMLR Journal 2025 Journal Article

Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision

Delaram Pirhayatifard
Arlei Silva

Graph Anomaly Detection (GAD) has demonstrated great effectiveness in identifying unusual patterns within graph-structured data. However, while labeled anomalies are often scarce in emerging applications, existing supervised GAD approaches are either ineffective or not applicable when moved across graph domains due to distribution shifts and heterogeneous feature spaces. To address these challenges, we present GADT3, a novel test-time training framework for cross-domain GAD. GADT3 combines supervised and self-supervised learning during training while adapting to a new domain during test time using only self-supervised learning by leveraging a homophily-based affinity score that captures domain-invariant properties of anomalies. Our framework introduces four key innovations to cross-domain GAD: an effective self-supervision scheme, an attention-based mechanism that dynamically learns edge importance weights during message passing, domain-specific encoders for handling heterogeneous features, and class-aware regularization to address imbalance. Experiments across multiple cross-domain settings demonstrate that GADT3 significantly outperforms existing approaches, achieving average improvements of over 8.2\% in AUROC and AUPRC compared to the best competing model.

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AAMAS Conference 2023 Conference Paper

Feature-based Individual Fairness in k-clustering

Debajyoti Kar
Mert Kosan
Debmalya Mandal
Sourav Medya
Arlei Silva
Palash Dey
Swagato Sanyal

Ensuring fairness in machine learning algorithms is a challenging and essential task. We consider the problem of clustering a set of points while satisfying fairness constraints. While there have been several attempts to capture group fairness in the 𝑘-clustering problem, fairness at an individual level is not so well-studied. We introduce a new notion of individual fairness in 𝑘-clustering based on features not necessarily used for clustering. The problem is NPhard and does not admit a constant factor approximation. Therefore, we design a randomized heuristic algorithm. Our experimental results against six competing baselines validate that our algorithm produces individually fairer clusters than the fairest baseline.

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AAAI Conference 2021 Conference Paper

Group Testing on a Network

Arlei Silva
Ambuj Singh

Group testing—where multiple samples are tested together using a single test kit and individual tests are performed only for samples in positive groups—is a popular strategy to optimize the use of testing resources. We investigate how to effectively group samples for testing based on a transmission network. We formalize the group assembling problem as a graph partitioning problem, where the goal is to minimize the expected number of tests needed to screen the entire network. The problem is shown to be computationally hard and thus we focus on designing effective heuristics for it. Using realistic epidemic models on real contact networks, we show that our approaches save up to 33% of resources—compared to the best baseline—at 4% prevalence, are still effective at higher prevalence, and are robust to missing transmission data.

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AAMAS Conference 2021 Conference Paper

Network Robustness via Global k -cores

Palash Dey
Suman Kalyan Maity
Sourav Medya
Arlei Silva

Network robustness is a measure a network’s ability to survive adversarial attacks. But not all parts of a network are equal. Kcores, which are dense subgraphs, are known to capture some of the key properties of many real-life networks. Therefore, previous work has attempted to model network robustness via the stability of its k-core. However, these approaches account for a single core value and thus fail to encode a global network resilience measure. In this paper, we address this limitation by proposing a novel notion of network resilience that is defined over all cores. In particular, we evaluate the stability of the network under node removals with respect to each node’s initial core. Our goal is to compute robustness via a combinatorial problem: find b most critical nodes to delete such that the number of nodes that fall from their initial cores is maximized. One of our contributions is showing that it is NPhard to achieve any polynomial factor approximation of the given objective. We also present a fine-grained complexity analysis of this problem under the lens of parameterized complexity theory for several natural parameters. Moreover, we show two applications of our notion of robustness: measuring the evolution of species and characterizing networks arising from different domains.

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IJCAI Conference 2020 Conference Paper

A Game Theoretic Approach For Core Resilience

Sourav Medya
Tiyani Ma
Arlei Silva
Ambuj Singh

K-cores are maximal induced subgraphs where all vertices have degree at least k. These dense patterns have applications in community detection, network visualization and protein function prediction. However, k-cores can be quite unstable to network modifications, which motivates the question: How resilient is the k-core structure of a network, such as the Web or Facebook, to edge deletions? We investigate this question from an algorithmic perspective. More specifically, we study the problem of computing a small set of edges for which the removal minimizes the k-core structure of a network. This paper provides a comprehensive characterization of the hardness of the k-core minimization problem (KCM), including innaproximability and parameterized complexity. Motivated by these challenges, we propose a novel algorithm inspired by Shapley value---a cooperative game-theoretic concept--- that is able to leverage the strong interdependencies in the effects of edge removals in the search space. We efficiently approximate Shapley values using a randomized algorithm with probabilistic guarantees. Our experiments, show that the proposed algorithm outperforms competing solutions in terms of k-core minimization while being able to handle large graphs. Moreover, we illustrate how KCM can be applied in the analysis of the k-core resilience of networks.

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