אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Message Passing Graph Neural Networks (MPGNNs) have emerged as the standard method for modeling complex interactions across diverse graph entities. While the theory of such models is widely investigated, their aggregation module has not received sufficient attention. Sum-based aggregators have solid theoretical foundations regarding their separation capabilities. However, practitioners often prefer using more complex aggregations and mixtures of diverse aggregations. In this research, we unveil a possible explanation for this gap. We claim that sum-based aggregators fail to "mix" features belonging to distinct neighbors, preventing them from succeeding at downstream tasks. To this end, we introduce Sequential Signal Mixing Aggregation (SSMA), a novel plug-and-play aggregation for MPGNNs. SSMA treats the neighbor features as 2D discrete signals and sequentially convolves them, inherently enhancing the ability to mix features attributed to distinct neighbors. By performing extensive experiments, we show that combining SSMA with well-established MPGNN architectures achieves substantial performance gains across various benchmarks, achieving new state-of-the-art results in many settings.