Yijie Teng | Searcharxiv

arXiv2023

Cooperative Network Learning for Large-Scale and Decentralized Graphs

Graph research, the systematic study of interconnected data points represented as graphs, plays a vital role in capturing intricate relationships within networked systems. However, in the real world, as graphs scale up, concerns about data security among different data-owning agencies arise, hindering information sharing and, ultimately, the utilization of graph data. Therefore, establishing a mutual trust mechanism among graph agencies is crucial for unlocking the full potential of graphs. Here, we introduce a Cooperative Network Learning (CNL) framework to ensure secure graph computing for various graph tasks. Essentially, this CNL framework unifies the local and global perspectives of GNN computing with distributed data for an agency by virtually connecting all participating agencies as a global graph without a fixed central coordinator. Inter-agency computing is protected by various technologies inherent in our framework, including homomorphic encryption and secure transmission. Moreover, each agency has a fair right to design or employ various graph learning models from its local or global perspective. Thus, CNL can collaboratively train GNN models based on decentralized graphs inferred from local and global graphs. Experiments on contagion dynamics prediction and traditional graph tasks (i.e., node classification and link prediction) demonstrate that our CNL architecture outperforms state-of-the-art GNNs developed at individual sites, revealing that CNL can provide a reliable, fair, secure, privacy-preserving, and global perspective to build effective and personalized models for network applications. We hope this framework will address privacy concerns in graph-related research and integrate decentralized graph data structures to benefit the network research community in cooperation and innovation.

cs.LG↗

arXiv2023

Stock network inference: A framework for market analysis from topology perspective

From a complex network perspective, investigating the stock market holds paramount significance as it enables the systematic revelation of topological features inherent in the market. This approach is crucial in exploring market interconnectivity, systemic risks, portfolio management, and structural evolution. However, prevailing methodologies for constructing networks based on stock data rely on threshold filtering, often needing help to uncover intricate underlying associations among stocks. To address this, we introduce the Stock Network Inference Framework (SNIF), which leverages a self-encoding mechanism. Specifically, the Stock Network Inference Encoder (SNIE) facilitates network construction, while the Movement Prediction Decoder (MPD) enhances movement forecasting. This integrated process culminates in the inference of a stock network, exhibiting remarkable performance across applications such as market structure analysis, stock movement prediction, portfolio construction, and community evolution analysis. Our approach streamlines the automatic construction of stock networks, liberating the process from threshold dependencies and eliminating the need for additional financial indicators. Incorporating Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) models within the SNIF framework, we effectively unearth deep-seated associations among stocks, augmenting the toolset available for comprehensive financial market research. This integration empowers our methodology to automatically construct stock networks without threshold dependencies or reliance on additional economic indicators.

physics.soc-ph↗