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The multi-relational social network is a new social network, which is proposed in recent years and more complex than single relational social network. Therefore, the existing community discovery algorithm is lack and their results are crude. The paper proposes a new algorithm for discovering community in multi-relational social network based on modified common neighbors similarity. The core idea is the more common neighbors are shared between two nodes, the more similar the nodes are. Finally, we test our generalized algorithm on an artificial sparse network in four dimensions.

In this paper, the scheduling problem involving optimization of multiple criteria (or objectives) is explored. There are many variants of the problem. The particular variant, in which the objectives are aggregated into a scalar function (with each criterion having weight which denotes its relative importance), is considered. An algorithm which can be used to solve very large classes of the multicriteria scheduling problem is proposed. The proposed algorithm and two solution methods selected from the literature were evaluated on a total of 900 randomly generated multicriteria scheduling problems (ranging from 10 to 500 jobs). Two variants of the release dates (0 – 24 and 0 – 49) are utilized. Results show that the proposed algorithm performed better than the selected solution methods when the total completion time criterion is much more important than the other criteria. However, when the total completion time criterion is much less important than the other criteria, the selected solution methods outperformed the proposed algorithm. The results are consistent under the two variants of the release dates.