In this paper we explore the application of text similarity for building text-rich knowledge graphs, where nodes describe concepts that relate semantically to each other. Semantic text similarity is a basic task in natural language processing (NLP) that aims at measuring the semantic relatedness of two texts. Transformer-based encoders like BERT combined with techniques like contrastive learning are currently the state-of-the-art methods in the literature. However, these methods act as black boxes where the similarity score between two texts cannot be directly explained from their components (e.g., words or sentences). In this work, we propose a method for similarity explainability for texts that are semantically connected to each other in a knowledge graph. To demonstrate the usefulness of this method, we use the Agenda 2030 which consists of a graph of sustainable development goals (SDGs), their subgoals and the indicators proposed for their achievement. Experiments carried out on this dataset show that the proposed explanations not only provide us with explanations about the computed similarity score but also they allow us to improve the accuracy of the predicted links between concepts.

In this paper, we address the challenges posed by dynamic networks in various domains, such as bioinformatics, social network analysis, and computer vision, where relationships between entities are represented by temporal graphs that respect a temporal order. To understand the structure and functionality of such systems, we focus on small subgraph patterns, called motifs, which play a crucial role in understanding dissemination processes in dynamic networks that can be a spread of fake news, infectious diseases or computer viruses. To address this, we propose a novel approach called temporal motif filtering for classifying dissemination processes in labeled temporal graphs. Our approach identifies and examines key temporal subgraph patterns, contributing significantly to our understanding of dynamic networks. To further enhance classification performance, we combined directed line transformations with temporal motif removal. Additionally, we integrate filtering motifs, directed edge transformations, and transitive edge reduction. Experimental results demonstrate that our proposed approaches consistently improve classification accuracy across various datasets and tasks. These findings hold the potential to unlock deeper insights into diverse domains and enable the development of more accurate and efficient strategies to address challenges related to spreading process in dynamic environments. Our work significantly contributes to the field of temporal graph analysis and classification, opening up new avenues for advancing our understanding and utilization of dynamic networks.

Streaming video segmentation decreases processing time by creating supervoxels taking into account small parts of the video instead of using all video content. Thanks to the good performance of the Iterative Spanning Forest to compute Supervoxels (ISF2SVX) based on Dynamic Iterative Spanning Forest (DISF) for video segmentation framework we propose a new graph-based streaming video segmentation method for supervoxel generation by using dynamic iterative spanning forest framework, so-called StreamISF, based on a pipeline composed of six stages: (1) formation of the graph for each block of the video; (2) seed oversampling; (3) IFT-based supervoxel design; (4) reduction in the number of supervoxels; (5) spread of trees; and (6) creation of the segmented video. The difference in our proposed method is that it is not necessary to have all the video in memory and the only previous information necessary to segment a block is the intersection frame between the blocks. Moreover, experimental results show that StreamISF creates supervoxels maintaining temporal coherence producing very competitive measures when compared to the state-of-the-art.