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IADIS International Conference WWW/Internet 2010respectively. The transaction value and the score given by one’s transaction partner are also associated withthe corresponding edge. The induced graph is called transaction social network. It is easy to draw theconclusion: directed edges appear in pairs. For any vertex, the income degree is equal to the outcome degree.Almost all the C2C community member reputation computing methods are based on transaction time. It isunfair to new comers. A natural idea is that sort transactions randomly and update one’s reputation based onthe new transaction sequence. However, this simple strategy will bring new unfairness. In this paper, weiteratively select a transaction employing the random walk strategy on the transaction social network andupdate one’s reputation until the reputation value of all nodes in the network are stable.The algorithm first induces the transaction social network topology from C2C e-commerce transaction data.Then it divides the network into several blocks using a social network analysis algorithm. After completing theabove preliminary work, it randomly selects one node from each block as seeds and applies random walkstrategy on the network with each seed as a starting point. The algorithm iteratively performs above procedureuntil the reputation of all nodes converges. The exact algorithm lists as follows.getNextNode(v 1 )->v 2getNextNode(v)->v 1v 2v 1getNextNode(v 3 )->NULLv3vgetNextNode(v 2 )->v 3Figure 1. Random walk path example (bold path), where v is the seed, the path extends from v and ends at v 3 .Algorithm: computing reputation based on transaction social network random walkStep 1: induce transaction social network N based on the transaction data;Step 2: compute a community set C={c 1,c 2,…,c k} (k• 1) of network N using thecommunity analysis algorithm based on maximum modularity;Step 3: do {Step 4:random select one node from each community in C, and get k nodes:n 1,n 2,…,n k;Step 5: for ( int i=1; i