论文标题
相互依赖网络中的子图检测的块结构化优化
Block-Structured Optimization for Subgraph Detection in Interdependent Networks
论文作者
论文摘要
我们为块结构的非convex优化提出了一个广义框架，该框架可以应用于相互依存网络中的结构化子图检测，例如多层网络，时间网络，网络网络等。具体而言，我们设计了一种有效，高效且可行的投影算法，即图形结构梯度投影（GBGP），以优化受图形结构约束的一般非线性函数。我们证明我们的算法：1）在网络大小上几乎在线时间运行； 2）享受理论上的近似保证。此外，我们演示了如何将我们的框架应用于两个非常实用的应用，并进行全面的实验以显示我们提出的算法的有效性和效率。
We propose a generalized framework for block-structured nonconvex optimization, which can be applied to structured subgraph detection in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. Specifically, we design an effective, efficient, and parallelizable projection algorithm, namely Graph Block-structured Gradient Projection (GBGP), to optimize a general non-linear function subject to graph-structured constraints. We prove that our algorithm: 1) runs in nearly-linear time on the network size; 2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications and conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm.
