Cycle Invariant Positional Encoding for Graph Representation Learning

Cycles are fundamental elements in graph-structured data and have demonstrated their effectiveness in enhancing graph learning models. To encode such information into a graph learning framework, prior works often extract a summary quantity, ranging from the number of cycles to the more sophisticated persistence diagram summaries. However, more detailed information, such as which edges are encoded in a cycle, has not yet been used in graph neural networks. In this paper, we make one step towards addressing this gap, and propose a structure encoding module, called CycleNet, that encodes cycle information via edge structure encoding in a permutation invariant manner. To efficiently encode the space of all cycles, we start with a cycle basis (i.e., a minimal set of cycles generating the cycle space) which we compute via the kernel of the 1-dimensional Hodge Laplacian of the input graph. To guarantee the encoding is invariant w.r.t. the choice of cycle basis, we encode the cycle information via the orthogonal projector of the cycle basis, which is inspired by BasisNet proposed by Lim et al. We also develop a more efficient variant which however requires that the input graph has a unique shortest cycle basis. To demonstrate the effectiveness of the proposed module, we provide some theoretical understandings of its expressive power. Moreover, we show via a range of experiments that networks enhanced by our CycleNet module perform better in various benchmarks compared to several existing SOTA models.Comment: Accepted as oral presentation in the Learning on Graphs Conference (LoG 2023

STGC3 inhibits xenograft tumor growth of nasopharyngeal carcinoma cells by altering the expression of proteins associated with apoptosis

STGC3 is a potential tumor suppressor that inhibits the growth of the nasopharyngeal carcinoma cell line CNE2; the expression of this protein is reduced in nasopharyngeal carcinoma compared with normal nasopharyngeal tissue. In this study, we investigated the tumor-suppressing activity of STGC3 in nude mice injected subcutaneously with Tet/pTRE-STGC3/CNE2 cells. STGC3 expression was induced by the intraperitoneal injection of doxycycline (Dox). The volume mean of Tet/pTRE-STGC3/CNE2+Dox xenografts was smaller than that of Tet/pTRE/CNE2+Dox xenografts. In addition, Tet/pTRE-STGC3/CNE2+Dox xenografts showed an increase in the percentage of apoptotic cells, a decrease in Bcl-2 protein expression and an increase in Bax protein expression. A proteomic approach was used to assess the protein expression profile associated with STGC3-mediated apoptosis. Western blotting confirmed the differential up-regulation of prohibitin seen in proteomic analysis. These results indicate that overexpression of STGC3 inhibits xenograft growth in nude mice by enhancing apoptotic cell death through altered expression of apoptosis-related proteins such as Bcl-2, Bax and prohibitin. These data contribute to our understanding of the function of STGC3 in human nasopharyngeal carcinoma and provide new clues for investigating other STGC3-associated tumors