In social online platforms, identifying influential seed users to maximize influence spread is a crucial as it can greatly diminish the cost and efforts required for information dissemination. While effective, traditional methods for Multiplex Influence Maximization (MIM) have reached their performance limits, prompting the emergence of learning-based approaches. These novel methods aim for better generalization and scalability for more sizable graphs but face significant challenges, such as (1) inability to handle unknown diffusion patterns and (2) reliance on high-quality training samples. To address these issues, we propose the Reinforced Expert Maximization framework (REM). REM leverages a Propagation Mixture of Experts technique to encode dynamic propagation of large multiplex networks effectively in order to generate enhanced influence propagation. Noticeably, REM treats a generative model as a policy to autonomously generate different seed sets and learn how to improve them from a Reinforcement Learning perspective. Extensive experiments on several real-world datasets demonstrate that REM surpasses state-of-the-art methods in terms of influence spread, scalability, and inference time in influence maximization tasks.

This paper presents a comprehensive study on the classification and detection of Silicosis-related lung inflammation. Our main contributions include 1) the creation of a newly curated chest X-ray (CXR) image dataset named SVBCX that is tailored to the nuances of lung inflammation caused by distinct agents, providing a valuable resource for silicosis and pneumonia research community; and 2) we propose a novel deep-learning architecture that integrates graph transformer networks alongside a traditional deep neural network module for the effective classification of silicosis and pneumonia. Additionally, we employ the Balanced Cross-Entropy (BalCE) as a loss function to ensure more uniform learning across different classes, enhancing the model's ability to discern subtle differences in lung conditions. The proposed model architecture and loss function selection aim to improve the accuracy and reliability of inflammation detection, particularly in the context of Silicosis. Furthermore, our research explores the efficacy of an ensemble approach that combines the strengths of diverse model architectures. Experimental results on the constructed dataset demonstrate promising outcomes, showcasing substantial enhancements compared to baseline models. The ensemble of models achieves a macro-F1 score of 0.9749 and AUC ROC scores exceeding 0.99 for each class, underscoring the effectiveness of our approach in accurate and robust lung inflammation classification.

Abstract--Network alignment (NA) is the task of discovering node correspondences across multiple networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their effectiveness is not without additional information such as prior anchor links and/or node features, which may not always be available due to privacy concerns or access restrictions. To tackle this challenge, we propose Grad-Align+, a novel NA method built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers a part of node pairs until all node pairs are found. In designing Grad-Align+, we account for how to augment node features in the sense of performing the NA task and how to design our NA method by maximally exploiting the augmented node features. To achieve this goal, Grad-Align+ consists of three key components: 1) centrality-based node feature augmentation (CNFA), 2) graph neural network (GNN)-aided embedding similarity calculation alongside the augmented node features, and 3) gradual NA with similarity calculation using aligned cross-network neighbor-pairs (ACNs). Through comprehensive experiments, we demonstrate that Grad-Align+ exhibits (a) the superiority over benchmark NA methods, (b) empirical validations as well as our theoretical findings to see the effectiveness of CNFA, (c) the influence of each component, (d) the robustness to network noises, and (e) the computational efficiency.

In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often unknown, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we present META-CODE, a unified framework for detecting overlapping communities in networks with unknown topology via exploratory learning aided by easy-to-collect node metadata. Specifically, META-CODE consists of three iterative steps in addition to the initial network inference step: 1) node-level community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) network exploration via community-affiliation-based node queries, and 3) network inference using an edge connectivity-based Siamese neural network model from the explored network. Through extensive experiments on five real-world datasets including two large networks, we demonstrated: (a) the superiority of META-CODE over benchmark community detection methods, achieving remarkable gains up to 151.27% compared to the best existing competitor, (b) the impact of each module in META-CODE, (c) the effectiveness of node queries in META-CODE based on empirical evaluations and theoretical findings, (d) the convergence of the inferred network, and (e) the computational efficiency of META-CODE.

Object detection has long been a topic of high interest in computer vision literature. Motivated by the fact that annotating data for the multi-object tracking (MOT) problem is immensely expensive, recent studies have turned their attention to the unsupervised learning setting. In this paper, we push forward the state-of-the-art performance of unsupervised MOT methods by proposing UnsMOT, a novel framework that explicitly combines the appearance and motion features of objects with geometric information to provide more accurate tracking. Specifically, we first extract the appearance and motion features using CNN and RNN models, respectively. Then, we construct a graph of objects based on their relative distances in a frame, which is fed into a GNN model together with CNN features to output geometric embedding of objects optimized using an unsupervised loss function. Finally, associations between objects are found by matching not only similar extracted features but also geometric embedding of detections and tracklets. Experimental results show remarkable performance in terms of HOTA, IDF1, and MOTA metrics in comparison with state-of-the-art methods.

Automatically classifying cough sounds is one of the most critical tasks for the diagnosis and treatment of respiratory diseases. However, collecting a huge amount of labeled cough dataset is challenging mainly due to high laborious expenses, data scarcity, and privacy concerns. In this work, our aim is to develop a framework that can effectively perform cough classification even in situations when enormous cough data is not available, while also addressing privacy concerns. Specifically, we formulate a new problem to tackle these challenges and adopt few-shot learning and federated learning to design a novel framework, termed F2LCough, for solving the newly formulated problem. We illustrate the superiority of our method compared with other approaches on COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average F1-Score of 86%. Our results show the feasibility of few-shot learning combined with federated learning to build a classification model of cough sounds. This new methodology is able to classify cough sounds in data-scarce situations and maintain privacy properties. The outcomes of this work can be a fundamental framework for building support systems for the detection and diagnosis of cough-related diseases.

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods.

In real-world applications of influence maximization (IM), the network structure is often unknown. In this case, we may identify the most influential seed nodes by exploring only a part of the underlying network given a small budget for node queries. Motivated by the fact that collecting node metadata is more cost-effective than investigating the relationship between nodes via queried nodes, we develop IM-META, an end-to-end solution to IM in networks with unknown topology by retrieving information from both queries and node metadata. However, using such metadata to aid the IM process is not without risk due to the noisy nature of metadata and uncertainties in connectivity inference. To tackle these challenges, we formulate an IM problem that aims to find two sets, i.e., seed nodes and queried nodes. We propose an effective method that iteratively performs three steps: 1) we learn the relationship between collected metadata and edges via a Siamese neural network model, 2) we select a number of inferred influential edges to construct a reinforced graph used for discovering an optimal seed set, and 3) we identify the next node to query by maximizing the inferred influence spread using a topology-aware ranking strategy. By querying only 5% of nodes, IM-META reaches 93% of the upper bound performance.

It has been insufficiently explored how to perform density-based clustering by exploiting textual attributes on social media. In this paper, we aim at discovering a social point-of-interest (POI) boundary, formed as a convex polygon. More specifically, we present a new approach and algorithm, built upon our earlier work on social POI boundary estimation (SoBEst). This SoBEst approach takes into account both relevant and irrelevant records within a geographic area, where relevant records contain a POI name or its variations in their text field. Our study is motivated by the following empirical observation: a fixed representative coordinate of each POI that SoBEst basically assumes may be far away from the centroid of the estimated social POI boundary for certain POIs. Thus, using SoBEst in such cases may possibly result in unsatisfactory performance on the boundary estimation quality (BEQ), which is expressed as a function of the $F$-measure. To solve this problem, we formulate a joint optimization problem of simultaneously finding the radius of a circle and the POI's representative coordinate $c$ by allowing to update $c$. Subsequently, we design an iterative SoBEst (I-SoBEst) algorithm, which enables us to achieve a higher degree of BEQ for some POIs. The computational complexity of the proposed I-SoBEst algorithm is shown to scale linearly with the number of records. We demonstrate the superiority of our algorithm over competing clustering methods including the original SoBEst.

An imprecise region is referred to as a geographical area without a clearly-defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters and the inclusion of a large portion of the undesirable region (i.e., a large number of noise points). To overcome these problems, we present DIR-ST$^2$, a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio--textual information but also temporal information on social media. Specifically, we aim at finding a proper radius of a circle used in the iterative DBSCAN process by gradually reducing the radius for each iteration in which the temporal information acquired from all resulting clusters are leveraged. Then, we propose an efficient and automated algorithm delineating the imprecise region via hierarchical clustering. Experiment results show that by virtue of the significant noise reduction in the region, our DIR-ST$^2$ method outperforms the state-of-the-art approach employing one-class support vector machine in terms of the $\mathcal{F}_1$ score from comparison with precisely-defined regions regarded as a ground truth, and returns apparently better delineation of imprecise regions. The computational complexity of DIR-ST$^2$ is also analytically and numerically shown.