We have observed that the S3GC model demonstrated commendable performance on large-scale graphs containing millions of nodes. This achievement is noteworthy. However, S3GC is primarily designed for unweighted networks. This raises the question of its applicability to weighted biological networks, which are prevalent in bioinformatics and where edge weights carry significant information. For example,
Liu R, Hirn M, Krishnan A. Accurately modeling biased random walks on weighted networks using node2vec+[J]. Bioinformatics, 2023, 39(1): btad047.
To adapt S3GC for weighted biological networks, several enhancements may be necessary to incorporate edge weights effectively. We would be grateful if you could provide some suggestions.
We have observed that the S3GC model demonstrated commendable performance on large-scale graphs containing millions of nodes. This achievement is noteworthy. However, S3GC is primarily designed for unweighted networks. This raises the question of its applicability to weighted biological networks, which are prevalent in bioinformatics and where edge weights carry significant information. For example,
Liu R, Hirn M, Krishnan A. Accurately modeling biased random walks on weighted networks using node2vec+[J]. Bioinformatics, 2023, 39(1): btad047.
To adapt S3GC for weighted biological networks, several enhancements may be necessary to incorporate edge weights effectively. We would be grateful if you could provide some suggestions.