Community search over directed graphs is a fundamental graph problem, with applications in various areas such as social network analysis, financial fraud detection, and biology. A common requirement in real applications is to return a community with a constrained size. However, most existing studies primarily focus on the cohesiveness of the community and ignore the constraint on community size. Therefore, this paper investigates the problem of searching (k_c,k_f) -truss community with a size constraint over directed graphs (denoted by SCTC), which aims to find a subgraph that satisfies csup ≥ k_c and fsup ≥ k_f among all connected subgraphs that contain the query vertex q and have at least l and at most h vertices, where q, l, h, k_c , and k_f are specified by the query. We develop an exact solution for SCTC search by exploiting the properties of (k_c,k_f) -truss subgraphs. The approach first identifies the maximal (k_c,k_f) -truss subgraph to narrow the search space, and then performs a refinement search within it to efficiently support the SCTC search. We investigate two search strategies, expansion and peeling. The expansion strategy guides the search order using a score function and optimizes the search strategy based on clique, while the peeling strategy designs a level-based mechanism to remove low-support edges one level at a time iteratively. Extensive experimental results on real-world datasets show that our proposed algorithm improves both the quality of the resulting community and the search efficiency.
Active learning, which lets the model select the most informative data within a limited annotation budget for annotation, often achieves comparable performance with fewer labelled samples. However, despite its potential benefits, active learning has not been extensively explored in Named Entity Recognition (NER), as NER is considered more challenging than traditional tasks commonly addressed in the active learning literature. In this paper, we propose a novel deep active learning framework called Deep AUC Maximisation-based Active Learning (DAMAL), which learns uncertainty-aware representations for unlabeled data. Notably, DAMAL, due to its foundation in AUC maximisation, excels in addressing label sparsity, making it a well-suited approach for NER tasks. Experimental results across several NER tasks highlight the advantages of the proposed framework over existing active learning methods for NER.
In multi-agent systems, the inappropriate behavior of agents may lead to a series of negative side effects (NSE) on the environment or other agents, thereby affecting the overall performance of the system. Existing methods for mitigating negative side effects predominantly adopt homogeneous cooperation paradigms, where agents are assumed to have aligned objectives and behave cooperatively. However, these methods critically fail to account for multi-agent scenarios involving heterogeneous objective conflicts, where agents pursue divergent or even conflicting objectives that complicate coordination and side effect mitigation. To address this issue, we present a Dynamic Adjustment Lexicographic Multi-Objective (DALMO) reinforcement learning approach that dynamically adjusts the degree of side effects avoidance based on the strategies of other agents. This approach estimates the side effects of other agents through belief modeling to adjust its own policy and uses a multi-objective integration to balance individual rewards and overall system performance. To evaluate the effectiveness of our approach, we designed a multi-agent grid world experiment in which agents have conflicting objectives. We compared our approach with existing approaches and the experimental results show that our approach achieves higher overall rewards in different scenarios with objective conflicts. This demonstrates that our approach enhances the overall performance of multi-agent systems with objective conflicts by employing corresponding side effect mitigation strategies.
Controlling disease risk (diagnostic errors) poses a significant challenge in the field of medicine, largely due to the inherent data imbalance between affected (positive) and healthy (negative) individuals. To address this challenge, we propose an effective classification strategy tailored for disease risk control. Our research aims to regulate the false negative rate (type I errors) while minimizing the false positive rate (type II errors). We employ the Neyman-Pearson paradigm, combining oversampling and feature selection techniques to balance the training dataset, thereby improving disease risk prediction accuracy. To evaluate our approach, we conduct comprehensive numerical experiments on an imbalanced Heart Failure Clinical Records dataset. The results demonstrate that oversampling techniques and feature selection can enhance the accuracy of disease risk control to a certain degree, and our proposed strategy achieves robust and commendable performance.
