Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). MC-GAT: Multi-Channel Graph Attention Networks for Capturing Diverse Information in Complex Graphs. Retrieved May 24, 2026, from https://4ort.xyz/entity/mc-gat-multi-channel-graph-attention-networks-for-capturing-diverse-information-in-complex-graphs
MLA“MC-GAT: Multi-Channel Graph Attention Networks for Capturing Diverse Information in Complex Graphs.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/mc-gat-multi-channel-graph-attention-networks-for-capturing-diverse-information-in-complex-graphs.
BibTeX@misc{4ortxyz_mc-gat-multi-channel-graph-attention-networks-for-capturing-diverse-information-in-complex-graphs_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{MC-GAT: Multi-Channel Graph Attention Networks for Capturing Diverse Information in Complex Graphs}}, year = {2026}, url = {https://4ort.xyz/entity/mc-gat-multi-channel-graph-attention-networks-for-capturing-diverse-information-in-complex-graphs}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): MC-GAT: Multi-Channel Graph Attention Networks for Capturing Diverse Information in Complex Graphs — https://4ort.xyz/entity/mc-gat-multi-channel-graph-attention-networks-for-capturing-diverse-information-in-complex-graphs (retrieved 2026-05-24)
