Community detection (CD) on signed networks is crucial for understanding how positive and negative relations jointly shape network structure. However, existing CD methods often yield inconsistent communities due to noisy or conflicting edge signs. In this paper, we propose ReCon, a model-agnostic post-processing framework that progressively refines community structures through four iterative steps: (1) structural refinement, (2) boundary refinement, (3) contrastive learning, and (4) clustering. Extensive experiments on eighteen synthetic and four real-world networks using four CD methods demonstrate that ReCon consistently enhances community detection accuracy, serving as an effective and easily integrable solution for reliable CD across diverse network properties.

In this paper, we first define information loss that occurs in the hypergraph expansion and then propose a novel framework, named MILEAGE, to evaluate hypergraph expansion methods by measuring their degree of information loss. MILEAGE employs the following four steps: (1) expanding a hypergraph; (2) performing the unsupervised representation learning on the expanded graph; (3) reconstructing a hypergraph based on vector representations obtained; and (4) measuring MILEAGE-score (i.e., mileage) by comparing the reconstructed and the original hypergraphs. To demonstrate the usefulness of MILEAGE, we conduct experiments via downstream tasks on three levels (i.e., node, hyperedge, and hypergraph): node classification, hyperedge prediction, and hypergraph classification on eight real-world hypergraph datasets. Through the extensive experiments, we observe that information loss through hypergraph expansion has a negative impact on downstream tasks and MILEAGE can effectively evaluate hypergraph expansion methods through the information loss and recommend a new method that resolves the problems of existing ones.

The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCNs) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node’s extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Specifically, TrustSGCN leverages topological information to measure trustworthiness on edge sign for high-order relationships inferred by balance theory. It then considers structural properties inherent to an input network, such as the ratio of triads, to correct for incorrect embedding propagation. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known social theories, i.e., balance and status, to jointly preserve the edge sign and direction between nodes connected by existing edges in the embedding space. The experiments on six real-world signed network datasets demonstrate that TrustSGCN consistently outperforms six state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN .

In this article, we propose a framework for embedding-based community detection on signed networks, namelyAdversarial learning ofBalanced triangle forCommunity detection, in short${{\sf ABC}}$. It first represents all the nodes of a signed network as vectors in low-dimensional embedding space and conducts a clustering algorithm (e.g.,k-means) on vectors, thereby detecting a community structure in the network. When performing the embedding process,${{\sf ABC}}$learns only the edges belonging to balanced triangles whose edge signs follow the balance theory, significantly excluding noise edges in learning. To address the sparsity of balanced triangles in a signed network,${{\sf ABC}}$learns not only the edges in balancedreal-triangles but those in balancedvirtual-triangles that do not actually exist but are produced by our generator. Finally,${{\sf ABC}}$employs adversarial learning to generate more-realistic balanced virtual-triangles with less noise edges. Through extensive experiments using seven real-world networks, we validate the effectiveness of (1) learning edges belonging to balanced real/virtual-triangles and (2) employing adversarial learning for signed network embedding. We show that${{\sf ABC}}$consistently and significantly outperforms the state-of-the-art community detection methods in all datasets.

In this paper, we propose a framework for embedding-based community detection on signed networks. It first represents all the nodes of a signed network as vectors in low-dimensional embedding space and conducts a clustering algorithm (e.g., k-means) on vectors, thereby detecting a community structure in the network. When performing the embedding process, our framework learns only the edges belonging to balanced triangles whose edge signs follow the balance theory, significantly excluding noise edges in learning. To address the sparsity of balanced triangles in a signed network, our framework learns not only the edges in balanced real-triangles but those in balanced virtual-triangles that are produced by our generator. Finally, our framework employs adversarial learning to generate more-realistic balanced virtual-triangles with less noise edges. Through extensive experiments using seven real-world networks, we validate the effectiveness of (1) learning edges belonging to balanced real/virtual-triangles and (2) employing adversarial learning for signed network embedding. We show that our framework consistently and significantly outperforms the state-of-the-art community detection methods in all datasets.

If we had the performance information of every application on every SSD, it would be very beneficial to both SSD users and SSD manufacturers. For SSD users, they can buy the SSD that is fastest for the most frequently using applications; for SSD manufacturers, they can figure out the strength and weakness of their SSD for every application. Toward this end, this article proposes a framework named${\sf FORESEE}$FORESEEthat estimates accurately the execution time of a given IO trace (i.e.,query IO trace) of a given application on a target SSDwithout its actual execution.${\sf FORESEE}$is developed based on the observation thatif two IO traces are similar to each other in their IO behavior, their execution times tend to be similar when they are executed on the same SSD. In${\sf FORESEE}$, the execution time of a query IO trace is estimated by using the execution times of the IO traces in a database similar to the query IO trace. Our technical contributions in${\sf FORESEE}$are as follows: (1) we propose a goodness function that efficiently evaluates the quality of sets of features that are used to measure the similarity of IO traces; (2) we propose a DB structure and a searching method for efficiently searching for similar IO traces to a query IO trace; (3) we propose an aggregation method that aggregates the execution times of similar IO traces to a query IO trace for accurately estimating the execution time of the query IO trace; and (4) we verify the effectiveness of${\sf FORESEE}$via extensive experiments by using real-world application IO traces. According to the results, the Pearson correlation coefficient (PCC) of the actual execution time and the estimated execution time by${\sf FORESEE}$is found to be 0.87, indicating${\sf FORESEE}$estimates the execution time accurately.

In this paper, we present CR-Graph (community reinforcement on graphs), a novel method that helps existing algorithms to perform more-accurate community detection (CD). Toward this end, CR-Graph strengthens the community structure of a given original graph by adding non-existent predicted intra-community edges and deleting existing predicted inter-community edges. To design CR-Graph, we propose the following two strategies: (1) predicting intra-community and inter-community edges (i.e., the type of edges) and (2) determining the amount of edges to be added/deleted. To show the effectiveness of CR-Graph, we conduct extensive experiments with various CD algorithms on 7 synthetic and 4 real-world graphs. The results demonstrate that CR-Graph improves the accuracy of all underlying CD algorithms universally and consistently.

With the NAND flash memory technology of solid-state drives (SSDs), the usage of SSDs is expanded to various devices. Due to the cost and time limitations of measuring the actual execution time of each application on SSDs, it is difficult for users to determine the best SSD for their most commonly used applications. In this paper, we propose a framework of estimating the execution time of an application IO trace (i.e., a query IO trace) on a target SSD without its real execution. Our framework is based on the observation that if two IO traces are similar in their IO behavior, their execution times tend to be similar when executed on the same SSD. The performance of the framework is evaluated through extensive experiments on real applications. The results show that our framework is accurate in estimating the execution time of an IO trace on SSDs.