Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging pathology technology which can detect the spatial distribution of up to 40 markers at cellular resolution. However, the high content and complexity of this spatial information is underutilized in downstream analysis. To overcome this limitation, we develop an interpretable graph convolutional network (GCN) trained by IMC data with 30 cell markers from 238 patient breast cancer samples, with both marker expressions and cell locations. The network enables triple negative breast cancer (TNBC) classification from other clinical types, including HR+HER2+, HR+HER- and HR-HER+. More importantly, with an embedded self-attention pooling module, cell communities with high diagnostic values can be detected based on the attention scores. The proposed GCN framework is benchmarked with a fully connected artifical neural network (ANN) without spatial information. With a stratified 8-fold cross validation, GCN performs slightly better than ANN for tissue-level classification (class balanced accuracy: 0.8283±0.0964 to 0.8123±0.0989; area under the curve: 0.8548±0.1252 to 0.8298±0.1407). Nevertheless, GCN remarkably outperforms ANN on potentially interested cell community detection, especially in TNBC tissues, regarding the spearman correlation coefficient (SCC) between attention scores and marker expressions. The average SCC differences between GCN and ANN range from 0.0532 to 0.1876 for Cytokeratin 5, 7, 14, 8/18, 19, and pan Cytokeration. With comparisons of selected markers on tissues with different clinical types and attention scores, the cell marker expressions correlate with their clinical types and diagnostic values, which further validate the proposed framework. Overall, our GCN enables interpretable triple negative breast cancer detection and has the potential to be widely implemented in other diseases and highly multiplexed imaging techniques for enhanced microenvironment analysis.