I am looking for a group-ranking algorithm (similar to Google's PageRank algorithm) that can help me solve the following problem:

In a large sparse graph every node can rate other nodes, an arc from node i to node j shows that i has rated j; and the arc-weight,r, which is a number in [0,1] shows how high the rating is. The graph is large (number of vertices>5000) and sparse (average number of adjacent arcs to each node is <5) many nodes do not have any adjacent arc coming in or going out and many only have incoming arcs, some only have outgoing arcs. Few have both incoming and outgoing arcs.

The question is how can we assign a ranking to each node such that the ranking is robust (a small group of nodes cannot push up their own ranking by down voting others)

I appreciate if you could please point out one or two valuable papers related to this question.

Note: If sparsity makes it difficult (or impossible) you can assume that the graph is dense.

Update It seems my problem definition was confusing. Here are some more explanations: Let's assume we have a web graph (although my problem is not related to the web at all). each page links to other pages. some pages are orphans (no incoming arc). But I want to rank the pages. Google does the same thing with their PageRank algorithm. Let's assume that the resulting Markov chain is irreducible and positive recurrent (it makes the problem much easier and better defined). The problem here is that each state rates only a subset of the state space.

The following are a number of related articles that I believe are very close:

1. Methodologies and Algorithms for Group-Rankings Decision
2. Country credit-risk rating aggregation via the separation-deviation model
3. HITS algorithm via @hakankj

I am looking for a public domain algorithm similar to HITS