Impact measurement for journals has become a popular parlor game, as well as impact factors like the 'h-index' for individual researchers. There are all kinds of problems with these measurements in general, and Eigenfactor does provide a way of eliminating some of the usual problems with measuring impact across multiple communities with different citation mechanisms, community sizes, and so on.
Eigenfactor has a few top 10 lists for different areas (science, social science, etc): here's my informal list of top ranked computer science algorithms (and friends) journals, ranked by article impact factor (all scores are percentiles over the universe of 6000+ journals considered):
- SIAM Review: 98.35
- J. ACM: 97.91
- IEEE Trans. Inf. Theory: 95.05
- Machine Learning: 93.92
- SICOMP: 93.04
- JCSS: 90.93
- Journal of Algorithms: 90.31*
- DCG: 85.29
- CGTA: 81.96
- Algorithmica: 79.13
* The ACM Transactions on Algorithms, which absorbed most of the editorial board of J. Alg, is too new to show up. This ranking should probably reflect the historical relevance of J. Alg as well as its current state.