Chromatin conformation capture with high-throughput sequencing (Hi-C) is a powerful technique to detect genome-wide chromatin interactions. While many computational methods have been developed to analyze one HiC sample at a time for interpretable patterns, relatively few methods are available to compare HiC data across different experiments. In this paper we introduce a novel approach to detect differentially interacting genomic regions between different HiC samples using a network model. To make data from different experiments comparable, we propose a normalization strategy based on network topological properties. We devise two measurements, using local and global connectivity information from the HiC interaction network, respectively, to assess the interaction difference between two samples for each genomic locus. When multiple replicates are present for each sample, our approach provides the exibility for users to either pool all replicates together to therefore increase the network coverage, or to use the replicates in parallel to increase the signal to noise ratio. We show that while the local method works better in detecting changes from simulated networks, the global approach performs better on real HiC in detecting genomic regions. Furthermore, the global and local methods, regardless of pooling, are always superior to two existing methods.