The propagation of aggressive behavior in online social networks presents a growing threat to digital well-being and social harmony. While existing research focuses on modeling aggression diffusion or detecting aggressive content, forecasting individual user aggression remains an open challenge. This work fills this gap by introducing Temporal Social Graph Attention Network (TSGAN), a social-aware sequence-to-sequence architecture designed to forecast aggressive behavior in dynamic social networks. The core of TSGAN is an adaptive socio-temporal attention module that dynamically models social influence and temporal dynamics. To capture global social influence, TSGAN employs a graph contrastive learning approach to generate global network context embeddings. TSGAN utilizes an aggression intensity metric derived from a proposed hybrid aggression content detection model (92.87% F1), combining a fine-tuned transformer with a large language model to quantify user aggression over time. TSGAN uniquely addresses user inactivity, models dynamic follower relationship impacts, and accounts for temporal behavioral decay while scaling to large networks. Experiments on real-world datasets (X for aggression forecasting and Flickr for popularity prediction) demonstrate TSGAN’s versatility and effectiveness. TSGAN outperforms baselines in forecasting across hourly, daily, and weekly temporal intervals, showing up to 24.8% improvement in daily aggression predictions.

Recent multimodal frameworks often grapple with semantic misalignment and noise, impeding effective integration of diverse modalities. In order to solve this problem, this study presents CaMN (Cross-aligned Multimodal Network), a framework designed to enhance multimodal understanding through a robust cross-alignment mechanism. Unlike conventional fusion methods, our framework aligns features extracted from images, text, and graphs via a tailored loss function, enabling seamless integration and exploitation of complementary information. Leveraging Abstract Meaning Representation (AMR), we extract intricate semantic structures from textual data, enriching the multi-modal representation with contextual depth. Furthermore, to enhance robustness, we employ a masked autoencoder to simulate noise-independent feature space. Through comprehensive evaluation on the crisisMMD dataset, CaMN demonstrates superior performance in crisis event classification tasks, highlighting its potential in advancing multimodal understanding across diverse domains. Our code is available at https://github.com/brillard1/CaMN.

Identifying and preserving community structures in a streaming graph is a very challenging task. However, many applications require the identification of these communities in very limited space and time. In this article, we design Community Sketch, a small space data structure that efficiently preserves communities. On query, it provides communities in constant time. With the use of community sketch data structure, a linear streaming community detection algorithm is proposed. Experimental results on the large real-world networks show that our algorithm outperforms other state-of-the-art algorithms in terms of quality metrics (NMI, F1-score, and WCC). Further, we propose an algorithm to produce benchmark network, namely, Temporal Community Benchmark Dataset (TCBD) which contains both true community labels and temporal information of edges. These synthetic networks are used to validate the proposed algorithm.

Proliferation of fake news has become a critical issue in today's information-driven society. Our study includes external knowledge from Wikidata which allows the model to cross-reference factual claims with established knowledge. This approach deviates from the reliance on social information to detect fake news that many state-of-the-art (SOTA) fact-checking models adopt. This paper introducesEA$^{2}$2N, anEvidence-basedAMR (abstract meaning representation)AttentionNetwork for Fake News Detection. EA$^{2}$N utilizes the proposed Evidence based Abstract Meaning Representation (WikiAMR) which incorporates knowledge using a proposed evidence-linking algorithm, pushing the boundaries of fake news detection. The proposed framework encompasses a combination of a novel language encoder and a graph encoder to detect fake news. While the language encoder effectively combines transformer-encoded textual features with affective lexical features, the graph encoder encodes semantic relations with evidence through external knowledge, referred to as WikiAMR graph. A path-aware graph learning module is designed to capture crucial semantic relationships among entities over evidence. Extensive experiments support our model's superior performance, surpassing SOTA methodologies with a difference of 2-3% in F1-score and accuracy for Politifact and Gossipcop datasets. The improvement due to the introduction of WikiAMR is found to be statistically significant with t-value less than 0.01.

As remote sensing capabilities continue to advance, there is a growing interest in leveraging computer vision and natural language processing for enhanced interpretation of remote sensing scenes. This paper explores the integration of textual information with images to augment traditional disaster classification methods. Our approach utilizes a predefined vision-language model to generate descriptive captions for images, fostering a more nuanced understanding of the remote sensing data. Next, we seamlessly integrate the generated textual information with image data through multimodal training, employing a multimodal deep learning method for disaster classification. The system categorizes input data into predefined disaster categories, presenting a comprehensive and accurate approach to emergency response system development. Experimental evaluations conducted on the AIDER dataset (Aerial Image Database for Emergency Response applications) showcase the efficacy of our approach, demonstrating improved accuracy compare to unimodal approach and reliability in disaster classification. This research contributes to the advancement of intelligent emergency response systems by harnessing the synergy between vision and language in the context of remote sensing.

The rapid advancement in deep learning-based object detection methods has made them a prevalent choice for real-time applications. Families of object detectors, including one-stage detectors, two-stage detectors, and region-based CNN networks, offer superior performance in accurately detecting objects. Despite their high accuracy, the complex design and black-box functionality of these models are not directly transferable in aerial imagery. Also, raise questions among users regarding the transparency of the algorithm in locating objects. Consequently, to demystify the decision process of these models, there is a need for Explainable AI (XAI) tools. XAI enables an understanding of the significance of each pixel in an image, shedding light on the contributions that lead to the model’s final output. In this context, this work will present an efficient, explainable, multi-scale vehicle detection network from high resolution aerial imagery, named as EVDNet. The EVDNet model has trained with two publicly available aerial image benchmark dataset DOTA and VEDAI. To enhance interpretability, we leverage XAI method using GradCam. The experimental results not only showcase the effectiveness and performance of the EVDNet model but also provide valuable insights into the object detection process. This research contributes to bridging the gap between complex object detection models and user understanding, offering a more transparent and interpretable approach to high-resolution aerial imagery analysis.

The paper proposes a Serverless and Mobile relay based architecture for a highly scalable Federated Learning system for low power IoT and Edge Devices. The aim is an easily deployable infrastructure on a public cloud platform by the end user and democratize the use of federated learning.

This paper describes a new approach of viewing a social relation as a string with various forces acting on it. Accordingly, a tension measure for a relation is defined. Various component forces of the tension measure are identified based on the structural information of the network. A new variant of rough set, namely, double bounded rough set, is developed in order to define these forces mathematically. It is revealed experimentally with synthetic and real-world data that positive and negative tension characterizes, relatively, the presence and absence of a physical link between two nodes. An algorithm based on tension measure is proposed for link prediction. Superiority of the algorithm is demonstrated on nine real-world networks, which include four temporal networks. The source code for calculating tension measure and link prediction algorithm is publicly available at https://gitlab.com/suman5/social-tension-measure.

The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose two new centrality measures, Diffusion Degree for independent cascade model of information diffusion and Maximum Influence Degree. Unlike other existing centrality measures, diffusion degree considers neighbors' contributions in addition to the degree of a node. The measure also works flawlessly with non uniform propagation probability distributions. On the other hand, Maximum Influence Degree provides the maximum theoretically possible influence (Upper Bound) for a node. Extensive experiments are performed with five different real life large scale directed social networks. With independent cascade model, we perform experiments for both uniform and non uniform propagation probabilities. We use Diffusion Degree Heuristic (DiDH) and Maximum Influence Degree Heuristic (MIDH), to find the top k influential individuals. k seeds obtained through these for both the setups show superior influence compared to the seeds obtained by high degree heuristics, degree discount heuristics, different variants of set covering greedy algorithms and Prefix excluding Maximum Influence Arborescence (PMIA) algorithm. The superiority of the proposed method is also found to be statistically significant as per T-test.