Semi-automatic anti-spam algorithms propagate either trust through links from a good seed set (e.g., TrustRank) or distrust through inverse links from a bad seed set (e.g., Anti-TrustRank) to the entire Web. These kinds of algorithms have shown their powers in combating link-based Web spam since they integrate both human judgement and machine intelligence. Nevertheless, there is still much space for improvement. One issue of most existing trust/distust propagation algorithms is that only trust or distrust is propagated and only a good seed set or a bad seed set is used. According to Wu et al. [2006a], a combined usage of both trust and distrust propagation can lead to better results, and an effective framework is needed to realize this insight. Another more serious issue of existing algorithms is that trust or distrust is propagated in nondifferential ways, that is, a page propagates its trust or distrust score uniformly to its neighbors, without considering whether each neighbor should be trusted or distrusted. Such kinds of blind propagating schemes are inconsistent with the original intention of trust/distrust propagation. However, it seems impossible to implement differential propagation if only trust or distrust is propagated. In this article, we take the view that each Web page has both a trustworthy side and an untrustworthy side, and we thusly assign two scores to each Web page: T-Rank, scoring the trustworthiness of the page, and D-Rank, scoring the untrustworthiness of the page. We then propose an integrated framework that propagates both trust and distrust. In the framework, the propagation of T-Rank/D-Rank is penalized by the target's current D-Rank/T-Rank. In other words, the propagation of T-Rank/D-Rank is decided by the target's current (generalized) probability of being trustworthy/untrustworthy; thus a page propagates more trust/distrust to a trustworthy/untrustworthy neighbor than to an untrustworthy/trustworthy neighbor. In this way, propagating both trust and distrust with target differentiation is implemented. We use T-Rank scores to realize spam demotion and D-Rank scores to accomplish spam detection. The proposed Trust-DistrustRank (TDR) algorithm regresses to TrustRank and Anti-TrustRank when the penalty factor is set to 1 and 0, respectively. Thus TDR could be seen as a combinatorial generalization of both TrustRank and Anti-TrustRank. TDR not only makes full use of both trust and distrust propagation, but also overcomes the disadvantages of both TrustRank and Anti-TrustRank. Experimental results on benchmark datasets show that TDR outperforms other semi-automatic anti-spam algorithms for both spam demotion and spam detection tasks under various criteria.

Propagating trust/distrust from a set of seed (good/bad) pages to the entire Web has been widely used to combat Web spam. It has been mentioned that a combined use of good and bad seeds can lead to better results. However, little work has been known to realize this insight successfully. A serious issue of existing algorithms is that trust/distrust is propagated in non-differential ways. However, it seems to be impossible to implement differential propagation if only trust or distrust is propagated. In this paper, we view that each Web page has both a trustworthy side and an untrustworthy side, and assign two scores to each Web page: T-Rank, scoring the trustworthiness, and D-Rank, scoring the untrustworthiness. We then propose an integrated framework which propagates both trust and distrust. In the framework, the propagation of T-Rank/D-Rank is penalized by the target's current D-Rank/T-Rank. In this way, propagating both trust and distrust with target differentiation is implemented. The proposed Trust-Distrust Rank (TDR) algorithm not only makes full use of both good seeds and bad seeds, but also overcomes the disadvantages of both existing trust propagation and distrust propagation algorithms. Experimental results show that TDR outperforms other typical anti-spam algorithms under various criteria.