Epilepsy is one of the most common clinical diseases, which is caused by abnormal discharge of brain nerves. Around 30% of patients will develop drug-resistant epilepsy that are hard to be cured by anti-epileptic drug treatment. This patient cohort are ideal candidate for surgical resection of the epileptic focus. For safety and maximum effective rate, the key to success of the operation is to identify the focus area and normal functional area accurately in the preoperative evaluation stage. Intracranial EEG (iEEG) has attracted much attention for its precise capture of the state of rapid brain activity and its strong locality. To automate the process of iEEG inspection and surgical evaluation, this paper propose a Gated Recurrent Unit-Granger Causality (GRU-GC) algorithm to detect effective connectivity between channels and construct a directed graph. From six local features, the top five feature combinations were selected to differentiate between epileptic foci and non-epileptic regions. Experiments indicate that these features are most discriminative during the ictal phase, yielding superior classification accuracy. Compared to traditional time-series-based methods, this study shows that GRU-GC algorithm is efficient in building effective graph model for improving preoperative epilepsy evaluations.