In response to the burgeoning cryptocurrency sector and its associated financial risks, there is a growing focus on detecting fraudulent activities and malicious addresses. Traditional studies are limited by their reliance on comprehensive historical data and address-wise manipulation, which are not available for early malice detection and fail to identify addresses controlled by the same fraudulent entity. We thus introduceEvolve Path Tracer, a novel solution designed for early malice detection in cryptocurrency. This system innovatively incorporates Asset Transfer Paths and corresponding path graphs in an evolve model, which effectively characterize rapidly evolving transaction patterns. First, for the target address, theClustering-based Path Selectorweight each Asset Transfer Path by finding sibling addresses along the Asset Transfer Paths.Evolve Path Encoder LSTMandEvolve Path Graph GCNthen encode the asset transfer path and path graph within a dynamic structure. Additionally, ourHierarchical Survival Predictorefficiently scales to predict the address labels, demonstrating high scalability and efficiency. We rigorously testedEvolve Path Traceron three real-world datasets of malicious addresses, where it consistently outperformed existing state-of-the-art methods. Our extensive scalability tests further confirmed the model's robust adaptability in dynamic prediction environments, highlighting its potential as a significant tool in the realm of cryptocurrency security.

Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective.In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC’s asset decentralization and design several decentralization degrees for quantification. Through extensive experiments, we emphasize the significant role of network properties and our network-based decentralization degree in enhancing Bitcoin analysis. Our findings demonstrate the importance of our comprehensive dataset and analysis in advancing research on Bitcoin’s transaction dynamics and decentralization, providing valuable insights into the network’s structure and its implications. The whole transaction data is available at dataset link.

Abstract Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation has been previously oversimplified as summarization of a fact-check article authored by fact-checkers. Therefore, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim (for Explainable fact-checking of real-world Claims), and introduce JustiLM, a novel few-shot Justification generation based on retrieval-augmented Language Model by using fact-check articles as an auxiliary resource during training only. Experiments show that JustiLM achieves promising performance in justification generation compared to strong baselines, and can also enhance veracity classification with a straightforward extension.1 Code and dataset are released at https://github.com/znhy1024/JustiLM.

Little attention has been paid on EArly Rumor Detection (EARD), and EARD performance was evaluated inappropriately on a few datasets where the actual early-stage information is largely missing.To reverse such situation, we construct BEARD, a new Benchmark dataset for EARD, based on claims from fact-checking websites by trying to gather as many early relevant posts as possible.We also propose HEARD, a novel model based on neural Hawkes process for EARD, which can guide a generic rumor detection model to make timely, accurate and stable predictions.Experiments show that HEARD achieves effective EARD performance on two commonly used general rumor detection datasets and our BEARD dataset.

Predicting human mobility is an important trajectory mining task for various applications, ranging from smart city planning to personalized recommendation system. While most of previous works adopt GPS tracking data to model human mobility, the recent fast-growing geo-tagged social media (GTSM) data brings new opportunities to this task. However, predicting human mobility on GTSM data is not trivial because of three challenges: 1) extreme data sparsity; 2) high order sequential patterns of human mobility and 3) evolving preference of users for tagging.