With the network methods and random matrix theory, we investigate the interaction structure of communities in financial markets. In particular, based on the random matrix decomposition, we clarify that the local interactions between the business sectors (subsectors) are mainly contained in the sector mode. In the sector mode, the average correlation inside the sectors is positive, while that between the sectors is negative. Further, we explore the time evolution of the interaction structure of the business sectors, and observe that the local interaction structure changes dramatically during a financial bubble or crisis.

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of the agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogenous personal preferences and interactions, these models are successful to explain the microscopic origination of the temporal and spatial correlations of the financial markets. We then present a novel paradigm combining the big-data analysis with the agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces, and develop an agent-based model to simulate the dynamic behaviors of the complex financial systems.