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In this work, with the help of a calculus system syllogistic logic with Carroll's diagrams (SLCD), we construct a useful algorithm for the possible deductions of polysyllogisms (soriteses). This algorithm makes a general deduction in categorical syllogisms with the help of diagrams to depict each proposition of polysyllogisms. The developed calculus system PolySLCD (PSLCD) is used to allow a formal deduction from premises set by comprising synchronically biliteral and triliteral diagrammatical appearance and simple algorithmic nature. This algorithm can be used to deduce new conclusions, step by step, through recursive conclusion sets that are obtained from premises of categorical polysyllogisms. The fundamental contributions of this paper are accurately deducing conclusions from sets corresponding to given premises as exact human reasoning using a single algorithm and designing this algorithm based on SLCD. Therefore, it is more suitable for computer-aided solution. Since the algorithm is set-based, it is a novel algorithm in the literature and it can easily contribute to the researchers using polysyllogisms in different scientific branches, such as computer science, decision-making systems and artificial intelligence. [ABSTRACT FROM AUTHOR]

Link prediction in heterogeneous networks aims at predicting missing interactions between pairs of nodes with the help of the topology of the target network and interconnected auxiliary networks. It has attracted considerable attentions from both computer science and bioinformatics communities in the recent years. In this paper, we introduce a novel Collaborative Linear Manifold Learning (CLML) algorithm. It can optimize the consistency of nodes similarities by collaboratively using the manifolds embedded between the target network and the auxiliary network. The experiments on four benchmark datasets have demonstrated the outstanding advantages of CLML, not only in the high prediction performance compared to baseline methods, but also in the capability to predict the unknown interactions in the target networks accurately and effectively. [ABSTRACT FROM AUTHOR]

Facing the massive amount of data appearing on the web, automatic analysis tools have become essential for web users to discover valuable information online. Precise similarity measurement plays a decisive role in enabling analysis tools to acquire high-quality performances. Because different features contribute diversely to similarity calculation, it is necessary to utilize weight to measure feature's contribution and import it into similarity measurement. To accurately assign feature's weight, constrained data-pairs provided by users are usually imported into the weight evaluation procedure, whereas conventional plans all fail to consider two challenges: (a) asymmetrical distribution of constrained data-pairs, and (b) inconsistency contained by constrained data-pairs. If these two issues occur, conventional plans are incompetent at addressing them or are even unable to work. Thus, this paper proposes a novel constraint based weight evaluation to address these two issues. For the former, constrained data-pairs are partitioned into several equivalent classes, and distributing parameters are assigned to constrained data-pairs to balance their distributions. For the latter, constrained data-pairs are connected one after another, and belief values are thereby formed to indicate their probability of being inconsistent. Experimental results demonstrate that this type of evaluation is independent of any algorithm. With this evaluation, similarities can be calculated more accurately. [ABSTRACT FROM AUTHOR]