The worst-case time complexity for generating all maximal cliques and computational experiments

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Computational approaches for detecting protein complexes from protein interaction networks: a survey (2010, methodology): “In the modified PPI network, PCP detects and merges dense subgraphs as protein complexes, using an effcient clique-finding algorithm from [41] and a partial clique merging.”
The specious art of single-cell genomics (2021, methodology): “To find cliques we used the find cliques function from the networkx package, which employs a variant [62] of the Bron & Kerbosch algorithm [63], to detect cliques in undirected graphs.”
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks (2021, background): “Message passing on clique complexes Generally, finding all the maximal cliques in a graph (Bron & Kerbosch, 1973; Tomita et al., 2006; Cazals & Karande, 2008) has an optimal worst-case complexity of O(3).”
Listing All Maximal Cliques in Sparse Graphs in Near-optimal Time (2010, background): “We observe that the problem is fixed-parameter tractable in terms of the degeneracy of the graph, a parameter that is expected to be low in many real-world applications, and that a slight modification of the Bron–Kerbosch algorithm performs optimally in terms of the degeneracy.”
Listing All Maximal Cliques in Large Sparse Real-World Graphs (2011, methodology): “[45] on graphs of small to moderate size, due to its use of the adjacency matrix representation.”