Geopolitical conflicts significantly impact financial networks and systems, e.g., Russia and Ukraine. Cryptoeconomic blockchains such as Bitcoin and Ethereum were introduced as substitutes for traditional financial systems and might behave differently under significant stress. The Russia–Ukraine conflict allowed us to analyze the impact of such complex geopolitical conflicts on the user behaviors of cryptoeconomic blockchains. This article investigates the early stage of such geopolitical conflict using time-varying graphs. We collected and analyzed all the transactions for Bitcoin and Ethereum that took place 2 weeks before and after the conflict started, i.e., we focused on what can be defined as the acute impact of such an event. Our results suggest that the early stage of such geopolitical conflicts may significantly affect cryptoeconomic blockchains’ user behaviors. For instance, we detected that some users behaved more cautiously during the preconflict phase and resumed normalcy during the postconflict phase but exhibited a shift in their behavior. This article analyzes the relationship between the early stages of geopolitical conflicts and cryptoeconomic systems.

The potential of Bitcoin for money laundering and terrorist financing represents a significant challenge in law enforcement. In recent years, the use of privacy-improving CoinJoin transactions has grown significantly and helped criminal actors obfuscate Bitcoin money flows. In this study, we use unsupervised machine learning to analyze the complete Bitcoin user graph in order to identify suspicious actors potentially involved in illegal activities. In contrast to the existing studies, we introduce a novel set of features that we use to identify potential criminal activity more accurately. Furthermore, we apply our clustering algorithm to a CoinJoin-adjusted variant of the Bitcoin user graph, which enables us to analyze the network at a more detailed, user-centric level while still offering opportunities to address advanced privacy-enhancing techniques at a later stage. By comparing the results with our ground truth data set, we find that our improved clustering method is able to capture significantly more illicit activity within the most suspicious clusters. Finally, we find that users associated with illegal activities commonly have significant short paths to CoinJoin wallets and show tendencies toward outlier behavior. Our results have potential contributions to anti-money laundering efforts and combating the financing of terrorism and other illegal activities.

Knowledge Base Question Answering systems (KBQA) aim to find answers to natural language questions over a knowledge base. This work presents a template matching approach for Complex KBQA systems (C-KBQA) using the combination of Semantic Parsing and Neural Networks techniques to classify natural language questions into answer templates. An attention mechanism was created to assist a Tree-LSTM in selecting the most important information. The approach was evaluated on the LC-Quad 1, LC-Quad 2, ComplexWebQuestion, and WebQuestionsSP datasets, and the results show that our approach outperforms other approaches on three datasets.