A5100344522- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Advanced Bandit Algorithms Research
- Optimization and Search Problems
- Game Theory and Applications
- Caching and Content Delivery
- Data Management and Algorithms
- Machine Learning and Algorithms
- Complex Systems and Time Series Analysis
- Auction Theory and Applications
- Peer-to-Peer Network Technologies
- Distributed systems and fault tolerance
- Advanced Graph Neural Networks
- Privacy-Preserving Technologies in Data
- Theoretical and Computational Physics
- Advanced Data Storage Technologies
- Stochastic Gradient Optimization Techniques
- Complexity and Algorithms in Graphs
- Multi-Criteria Decision Making
- Reinforcement Learning in Robotics
- Spam and Phishing Detection
- Advanced Harmonic Analysis Research
- Topic Modeling
- Protein Structure and Dynamics
- Parallel Computing and Optimization Techniques
Peking University
2016-2025
Tianjin University of Traditional Chinese Medicine
2025
Zhejiang Industry Polytechnic College
2025
Guilin University of Electronic Technology
2018-2025
Nanjing University of Information Science and Technology
2025
Chinese University of Hong Kong
2024
Microsoft Research Asia (China)
2015-2024
National University of Defense Technology
2009-2024
Tsinghua University
2013-2024
University of Science and Technology Beijing
2024
Influence maximization is the problem of finding a small subset nodes (seed nodes) in social network that could maximize spread influence. In this paper, we study efficient influence from two complementary directions. One to improve original greedy algorithm [5] and its improvement [7] further reduce running time, second propose new degree discount heuristics improves spread. We evaluate our algorithms by experiments on large academic collaboration graphs obtained online archival database...
Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set seed nodes in social network that maximizes spread influence under certain cascade models. The scalability maximization key factor for enabling prevalent viral marketing large-scale online networks. Prior solutions, such as greedy algorithm Kempe et al. (2003) its improvements are slow not scalable, while other heuristic algorithms do provide consistently good performance on spreads....
Biological functions of sirtuins may involve lysine desuccinylase and demalonylase activities.
Building upon the OPLS3 force field we report on an enhanced model, OPLS3e, that further extends its coverage of medicinally relevant chemical space by addressing limitations in chemotype transferability. OPLS3e accomplishes this incorporating new parameter types recognize moieties with greater specificity and integrating on-the-fly parametrization approach to assignment partial charges. As a consequence, leads accuracy against performance benchmarks assess small molecule conformational...
Influence maximization is the problem of finding a small set most influential nodes in social network so that their aggregated influence maximized. In this paper, we study linear threshold model, one important models formalizing behavior propagation networks. We first show computing exact general networks model #P-hard, which closes an open left seminal work on by Kempe, Kleinberg, and Tardos, 2003. As contrast, directed cyclic graphs (DAGs) can be done time to size graphs. Based fast...
The problem of identifying rumors is practical importance especially in online social networks, since information can diffuse more rapidly and widely than the offline counterpart. In this paper, we identify characteristics by examining following three aspects diffusion: temporal, structural, linguistic. For temporal characteristics, propose a new periodic time series model that considers daily external shock cycles, where demonstrates rumor likely have fluctuations over time. We also key...
In this paper, we introduce a small variation to current approaches broadly called Taguchi Robust Design Methods. these methods, there are two broad categories of problems associated with simultaneously minimizing performance variations and bringing the mean on target, namely, Type I—minimizing in caused by noise factors (uncontrollable parameters). II—minimizing control (design variables). existing solve both types problems. This embodies integration Response Surface Methodology (RSM)...
Influence maximization is the problem of selecting top k seed nodes in a social network to maximize their influence coverage under certain diffusion models. In this paper, we propose novel algorithm IRIE that integrates advantages ranking (IR) and estimation (IE) methods for both independent cascade (IC) model its extension IC-N incorporates negative opinion propagations. Through extensive experiments, demonstrate matches other algorithms while scales much better than all algorithms....
Previous chapter Next Full AccessProceedings Proceedings of the 2012 SIAM International Conference on Data Mining (SDM)Influence Blocking Maximization in Social Networks under Competitive Linear Threshold ModelXinran He, Guojie Song, Wei Chen, and Qingye JiangXinran Jiangpp.463 - 474Chapter DOI:https://doi.org/10.1137/1.9781611972825.40PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In many real-world situations, different often opposite...
We study the quality of service (QoS) failure detectors. By QoS, we mean a specification that quantifies: (1) how fast detector detects actual failures and (2) well it avoids false detections. first propose set QoS metrics to specify detectors for systems with probabilistic behaviors, i.e., where message delays losses follow some probability distributions. then give new algorithm analyze its in terms proposed metrics. show that, among large class detectors, is optimal respect these Given...
Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set seed nodes in social network that maximizes spread influence under certain cascade models. In this paper, we propose an extension to independent model incorporates emergence propagation negative opinions. The new has explicit parameter called quality factor natural behavior people turning product due defects. Our negativity bias (negative opinions usually dominate over positive...
Significance Lysine succinylation is a recently discovered protein posttranslational modification and SIRT5 an efficient desuccinylase. Although many mammalian proteins have been found to be regulated by lysine SIRT5, the physiological significance of remains unknown. Here we report that predominantly accumulates in heart when Sirt5 deleted. -deficient mice exhibit defective fatty acid metabolism, decreased ATP production, hypertrophic cardiomyopathy. Our data suggest regulating metabolism...
Research on social networks has exploded over the last decade. To a large extent, this been fueled by spectacular growth of media and online networking sites, which continue grow
Influence maximization is a well-studied problem that asks for small set of influential users from social network, such by targeting them as early adopters, the expected total adoption through influence cascades over network maximized. However, almost all prior work focuses on single propagating entity or purely-competitive entities. In this work, we propose Comparative Independent Cascade (Com-IC) model covers full spectrum interactions competition to complementarity. Com-IC, users'...
Zhen Yang, Wei Chen, Feng Wang, Bo Xu. Proceedings of the 2018 Conference North American Chapter Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
In the context of digital economy, empowering firm innovation investment through transformation is an important strategy for promoting development in China. This study investigates effects and mechanisms on from perspective total factor productivity, using a sample manufacturing companies listed A-share market China 2012 to 2021. The following three main findings were obtained. First, results both fixed- random-effects regression methods revealed that significantly promotes firms'...
Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult find. This paper describes RaceTrack, a dynamic race detection tool that tracks the actions of program reports warning whenever suspicious pattern activity has been observed. RaceTrack uses novel hybrid algorithm employs an adaptive approach automatically directs more effort areas suspicious, thus providing accurate warnings for much less over-head. A post-processing step...
Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult find. This paper describes RaceTrack, a dynamic race detection tool that tracks the actions of program reports warning whenever suspicious pattern activity has been observed. RaceTrack uses novel hybrid algorithm employs an adaptive approach automatically directs more effort areas suspicious, thus providing accurate warnings for much less over-head. A post-processing step...
Abstract In this paper, a new Probabilistic Sensitivity Analysis (PSA) approach based on the concept of relative entropy is proposed for design under uncertainty. The method evaluates impact random variable performance by measuring divergence between two probability density functions response, obtained before and after variation reduction variable. can be applied both over whole distribution response [called global probabilistic sensitivity analysis (GRPSA)] in any interested partial range...