This work constructs multiple entity co-occurrence networks using two tools (Fast Entity Linker and DBpedia Spotlight) and numerous confidence thresholds for each and proves that standard network analysis metrics, such as size, connectivity, and centrality are all heavily influenced by the choice of entity linking tool.
Analysis of document corpora with complex internal and inter-document links requires not only retrieval of relevant texts but also construction of a compact, verifiable and traceable set of fragments sufficient for downstream reasoning and citation. The purpose of this work is to propose a fragment retrieval method for document corpora in which the meaning of a fragment is determined by its local content, position in the document hierarchy and links to other fragments. The method is based on a joint representation of the corpus as a tree of structural units and a directed link graph as well as on hybrid ranking that combines lexical search, vector similarity, and a link signal. The monotonicity and submodularity of the objective function are shown, which makes it possible to use greedy algorithms with a known approximation guarantee and to perform budgeted context selection for a Retrieval-Augmented Generation (RAG) system. In addition, an evaluation protocol is introduced that separates retrieval quality at the document, fragment, and citation levels. The method is formally validated on tests of lexical mismatch robustness and budgeted selection efficiency compared with simple strategies. Examples from the legal domain are used to illustrate the method. The method can be used as a retrieval layer for RAG systems in question answering, evidence retrieval, regulatory compliance, and analysis of large structurally connected corpora.
M. Ulizko, A. Beresnev, V. V. Zhukov et al.· Scientific and Technical Jou...· 0 citations
The results show that the proposed methodology is not limited to a single algorithm and permits the combination of direct extraction, specialized models, multi-stage pipelines, and OWL ontologies, which can be applied to the analysis of scientific, technical, and regulatory texts in a secure local environment.
O. I. Zakharova, K. N. Ivanov, S. P. Levashkin et al.· COMPUTATIONAL MATHEMATICS AN...· 0 citations
Social systems play a vital role through interactions around shared interests and the maintenance of relationships, which are part of society. These can be represented as networks, and their analysis plays a crucial role in dynamic environments, which can be achieved through link prediction and community detection tasks. Several link prediction approaches estimate new relationships, such as suggesting friends on social media. On the other hand, a social faction is a group of connected people that reveals the internal structure of the social system and can be identified through community detection approaches. The evolution of social systems can be understood using these approaches by analysing their structure and behaviour. To handle this, a combination of these approaches, along with interaction-based analysis, is used to form cohesive communities, thereby enabling the study of social systems dynamically. In addition, attribute information plays a critical role. Most real-world graphs, like Facebook and Twitter, have attribute information that provides context, and integrating them with structural information offers deeper insights into patterns, which is a critical task. However, existing approaches have limitations in handling dynamics of social systems. Additionally, no existing approaches in the literature help identify potential links and form densely connected communities for attributed graphs. To address these challenges, we have proposed an information workflow, i.e., inc-LPCDAG (\underline{inc}remental Link Prediction and Community Detection in Attributed Graphs). We conducted experiments to evaluate efficiency, and results demonstrate the effectiveness of our proposed workflow by analysing and understanding structural and behavioural processes.
Identifying interactions between biological entities is a cornerstone of molecular research, but assembling such lists from the literature is slow and tedious. For many research questions, no curated database exists, leaving researchers to survey the relevant literature themselves. We present interaction-finder, a tool that automates this process: given a topic string and user-defined entity types, it discovers relevant literature through llm-guided iterative search, extracts candidate associations from full-text articles, and produces a ranked list where every association is backed by quoted passages verified against the source text. A self-contained interactive html report enables rapid triage of the results. Evaluated across 60 topics in three domains (celltype–cellmarker, disease–gene, and ligand–receptor), interaction-finder recalls 1.2–4.3× as many known associations as single-shot prompting and an off-the-shelf deep-research framework, with all extracted quotes verified against source text. To assess candidates unrecognised from the gold-standard databases, we scored each candidate using an independent llm judge blind to the tool’s reasoning. Across the three domains, unverified candidates score similarly to gold-standard associations. We find the gold-standard associations are enriched at the top of our ranked candidates, with an overall recall@20 of 0.61. Interaction-finder is freely available at https://github.com/tecosaur/interaction_finder under an mit licence.
Source localization aims to locate the origin of failures and rumors in complex graphs, which has broad implications for financial systems. However, previous methods commonly assume that the propagation graph is known in advance. In practice, the graph on which failures or information propagate depends on relationships among assets and other factors that are difficult to observe. In this paper, we first construct a propagation proxy graph from the semantic similarity between financial posts by using a pretrained language model to encode their textual descriptions. We then design a two-stage framework for source localization based on a voting scheme. In the first stage, each activated node votes for the neighbor that may have triggered it. In the second stage, each node combines the votes received from its neighbors and determines whether it belongs to the source set. In the experiment, approximately 12,000 financial Twitter posts are encoded with a pretrained language model, and the propagation proxy graph is constructed by connecting the Top-k semantic neighbors of each node. The source-localization model is trained using susceptible–infected–recovered (SIR) trajectories and then applied to susceptible–infected (SI), independent cascade (IC), and linear threshold (LT) trajectories. This protocol simulates concept drift caused by a change in the underlying propagation mechanism. Across the three target diffusion settings, accuracy remains around 0.90, AUC exceeds 0.91, and macro-averaged precision ranges from 0.565 to 0.580. The experiments demonstrate that the proposed method remains effective when the diffusion mechanism changes after training.
