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However, these networks are usually shallow due to the over-smoothing of GCNs with many layers, which limits the expressive power of learning graph representations. The current methods of solving the limitations have the bottlenecks of high complexity and many parameters. Although Simple Graph Convolution (SGC) reduces the complexity and parameters, it fails to distinguish the feature information of neighboring nodes at different distances. To tackle the limits, we propose MulStepNET, a stronger multi-step graph convolutional network architecture, that can capture more global information, by simultaneously combining multi-step neighborhoods information. When compared to existing methods such as GCN and MixHop, MulStepNET aggregates neighborhoods information at more distant distances via multi-power adjacency matrix while fitting fewest parameters and being computationally more efficient. Experiments on citation networks including Pubmed, Cora, and Citeseer demonstrate that the proposed MulStepNET model improves over SGC by 2.8, 3.3, and 2.1% respectively while keeping similar stability, and achieves better performance in terms of accuracy and stability compared to other baselines.<\/jats:p>","DOI":"10.1007\/s12652-021-03355-x","type":"journal-article","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T15:03:24Z","timestamp":1628262204000},"page":"1017-1026","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MulStepNET: stronger multi-step graph convolutional networks via multi-power adjacency matrix combination"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0368-7971","authenticated-orcid":false,"given":"Xun","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangyuan","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqing","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"key":"3355_CR1","unstructured":"Abu-El-Haija S, Kapoor A, Perozzi B, Lee J (2019a) N-GCN: multi-scale graph convolution for semi-supervised node classification. 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