Network science has become an ever-increasing and interesting field of research in the recent decade by focusing on finding hidden knowledge in complex networks. This study of complex relationships in network structures has also gained a lot of interest in the world of finance and stock markets. The paper focuses on prediction in Tehran Stock Exchange (TSE) and analysis of the relationship between different sectors, looking into the market price indices data of different market sectors and their fluctuations over time. Four different network structures have been extracted from the TSE market data, two with association rules mining and two with Pearson cross-correlation. Using the correlation with different threshold cuts, different networks have been created and importance of market sectors have been analyzed using different network centrality measurements. Then, by using Apriori algorithms, association patterns in fluctuation of the price indices are extracted for building different directed networks. These directed networks are used in assessing current market dynamics as well as predicting future market price fluctuations that is tested through an evaluation method.