Результаты поиска по 'complex networks':
Найдено статей: 33
  1. Yevin I.A., Khabibullin T.F.
    Social networks
    Computer Research and Modeling, 2012, v. 4, no. 2, pp. 423-430

    The paper reviews the main results of the study of real social networks (networks of collaboration between scientists and actors, networks of citation of scientific publications, networks of friends and acquaintances, etc.) and modern online social networks (Twitter, Facebook etc.) from the complex networks theory standpoint. Based on original research by the authors, it reveals peculiarities of perception of certain complex networks.

    Keywords: scalefree networks, assortativity, online social networks.
    Views (last year): 6. Citations: 6 (RSCI).
  2. Strygin N.A., Kudasov N.D.
    Fast and accurate x86 disassembly using a graph convolutional network model
    Computer Research and Modeling, 2024, v. 16, no. 7, pp. 1779-1792

    Disassembly of stripped x86 binaries is an important yet non-trivial task. Disassembly is difficult to perform correctly without debug information, especially on x86 architecture, which has variablesized instructions interleaved with data. Moreover, the presence of indirect jumps in binary code adds another layer of complexity. Indirect jumps impede the ability of recursive traversal, a common disassembly technique, to successfully identify all instructions within the code. Consequently, disassembling such code becomes even more intricate and demanding, further highlighting the challenges faced in this field. Many tools, including commercial ones such as IDA Pro, struggle with accurate x86 disassembly. As such, there has been some interest in developing a better solution using machine learning (ML) techniques. ML can potentially capture underlying compiler-independent patterns inherent for the compiler-generated assembly. Researchers in this area have shown that it is possible for ML approaches to outperform the classical tools. They also can be less timeconsuming to develop compared to manual heuristics, shifting most of the burden onto collecting a big representative dataset of executables with debug information. Following this line of work, we propose an improvement of an existing RGCN-based architecture, which builds control and flow graph on superset disassembly. The enhancement comes from augmenting the graph with data flow information. In particular, in the embedding we add Jump Control Flow and Register Dependency edges, inspired by Probabilistic Disassembly. We also create an open-source x86 instruction identification dataset, based on a combination of ByteWeight dataset and a selection open-source Debian packages. Compared to IDA Pro, a state of the art commercial tool, our approach yields better accuracy, while maintaining great performance on our benchmarks. It also fares well against existing machine learning approaches such as DeepDi.

    Keywords: disassembly, machine learning, graph neural network, x86.
  3. Iakushkin O.O., Grishkin V.M.
    Visualization of work of a distributed application based on the mqcloud library
    Computer Research and Modeling, 2015, v. 7, no. 3, pp. 529-532

    Independent components communicating with each other due to complex control make the work of complex distributed computer systems poorly scalable within the framework of the existing communication middleware. Two major problems of such systems' scaling can be defined: overloading of unequal nodes due to proportional redistribution of workload and difficulties in the realization of continuous communication between several nodes of the system. This paper is focused on the developed solution enabling visualization of the work of such a dynamical system.

    Keywords: distributed systems, cloud computing, services, monitoring, networking, peer-to-peer.
    Citations: 1 (RSCI).
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