Data-enhanced revealing of trends in Geoscience

Purpose:This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023.By integrating bibliometric analysis with expert insights from the Deep-time Digital Earth(DDE)initiative,this article identifies key emerging themes shaping the landscape of Earth Sciences①.Design/methodology/approach:The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database.To map relationships between articles,citation networks were constructed,and spectral clustering algorithms were then employed to identify groups of related research,resulting in 407 clusters.Relevant research terms were extracted using the Log-Likelihood Ratio(LLR)algorithm,followed by statistical analyses on the volume of papers,average publication year,and average citation count within each cluster.Additionally,expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation,relevance,and impact within Geosciences,and finalize naming of these top trends with consideration of the content and implications of the associated research.This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists.Findings:Thirty significant trends were identified in the field of Geosciences,spanning five domains:deep space,deep time,deep Earth,habitable Earth,and big data.These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society,science,and technology.Research limitations:The analyzed data of this study only contain those were included in the Web of Science.Practical implications:This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science,especially on solid earth.The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study.Originality/value:This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.

Analyzing and De-Anonymizing Bitcoin Networks:An IP Matching Method with Clustering and Heuristics

The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research.Since Bitcoin was created by Nakamoto in 2009,it has,to some extent,deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations.In this paper,the power-law distribution that the Bitcoin network obeys is given with mathematical proof,while traditional deanonymous methods such as clustering fail to satisfy it.Therefore,considering the profit-oriented characteristics of Bitcoin traders in such occasion,we put forward a de-anonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT),with realtime exchange rate of Bitcoin involved.With heuristic approach used for de-anonymity,algorithm that deals with the adjacency matrix and transition probability matrix are also put forward,which then makes it possible to apply clustering to the IP matching method.Basing on the heuristic approach and additional algorithm for clustering,finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain.Experiments on IP matching method are applied to the actual data.It turns out that similar behavioral pattern between IP and transaction records are shown,which indicates the superiority of IP matching method.

EvolveKG:a general framework to learn evolving knowledge graphs

A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolving knowledge graphs whose structure temporally grows over time.However,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored.To this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones.EvolveKG quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge interaction.The novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain time.Particularly,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with time.Besides,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged.Under four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.

VSAN:A new visualization method for super-large-scale academic networks

As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology.With the booming development of science and technology,the number of papers has been growing exponentially.Just like the fact that Internet of Things(IoT)allows the world to be connected in a flatter way,how will the network formed by massive academic papers look like?Most existing visualization methods can only handle up to hundreds of thousands of node size,which is much smaller than that of academic networks which are usually composed of millions or even more nodes.In this paper,we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks(VSAN).Nodes can represent papers or authors while the edges means the relation(e.g.,citation,coauthorship)between them.In order to comprehensively improve the visualization effect,three levels of optimization are taken into account in the whole design of VSAN in a progressive manner,i.e.,bearing scale,loading speed,and effect of layout details.Our main contributions are two folded:(1)We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts,thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities.(2)We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels,with the ability to quickly zoom between different levels.In addition,we propose a“jumping between nebula graphs”method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks.Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes.The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously,but also returns some interesting observations that may drive extra attention from scientists.