Main-memory triangle computations for very large (sparse (power-law)) graphs

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Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks (2021, background): “In sparse graphs, this can be done efficiently since the best triangle listing algorithms are known to have O(m) complexity (Latapy, 2008), where m is the number of edges.”
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian (2018, background): “However, practical algorithms are typically faster on real-world graph data exhibiting common structural properties [15,82].”
EmptyHeaded: A Relational Engine for Graph Processing (2015, methodology): “icitly orders the nodes. The choice of ˇ(v i) aects the internal skew of a sets the graph. Node ordering may impact the performance of a class of symmetrical queries like triangle without selections [15,28,51]. The key idea of this method is given an undirected graph, one creates a new directed grap”
DOULION: counting triangles in massive graphs with a coin (2009, methodology): “Finally the compact − forward iterator ([20]) further improves the forward algorithm.”
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing (2019, background): “This leads to computing challenges [11]–[13] due to workload imbalance [14].”
Motifs in Temporal Networks (2016, methodology): “Use a fast static graph triangle enumeration algorithm to find all triangles in the static graph G induced by T [14].”