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It is challenging to deploy check-in nodes optimally in a complex network so as to perform specific check-in like services, e.g., fuel supplements etc., but crucial in many real-world applications. In this article, we propose and study the new optimization problem of placing check-in nodes with the minimum cost, i.e., the problem of finding the minimum-cost check-in nodes (MCCN), which is of great interests for real-world application situations, but even more difficult. We motivate the new algorithms through three typical worse cases by the often-used greedy-type algorithms, i.e., One-to-Many, Many-to-One, and Duplicate-Overrides. With this respect, the proposed novel optimization algorithms utilize a novel metric of contribution density for selecting check-in nodes iteratively, which successfully avoid the occurrence of the two worse cases of One-to-Many and Many-to-One. We also introduce an extra backward extraction step in one of new algorithms, which overcomes the crucial worse case of ``duplicate overrides'' and largely improves the algorithmic performance in solving the introduced optimization problem of MCCN. Meanwhile, we extend the new contribution density metric to a more general class of functions and study their effectiveness to eliminate the two worse cases of One-to-Many and Many-to-One; also, a detailed analysis on complexity and performance of the proposed algorithms is presented to show their numerical efficiency and accuracy. Extensive experiments over two classical artificial networks, i.e., BA network and ER network, and ten real-world networks, under two typical cost setups, show the proposed algorithms significantly outperform the four state-of-the-art algorithms. We also demonstrate that our proposed algorithms are much reliable and robust with different experiment settings of deployment cost and network-type and scale.

As the community detection is able to facilitate the discovery of hidden information in complex networks, it has been drawn a lot of attention recently. However, due to the growth in computational power and data storage, the scale of these complex networks has grown dramatically. In order to detect communities by utilizing global approaches, it is required to have all the global information of the whole network; something which is impossible, because of the rapid growth in the size of the networks. In this paper, a local approach has been proposed based on the detection and expansion of core nodes. First, a community’s central node (core node) which has a high level of embeddedness is detected based on the similarity between graph’s nodes. By using this, the total weights of a weighted graph’s edges created. Following by that, the expansion of these nodes will be considered, by utilizing the concept of node’s membership based on the definition of strong community for weighted graphs. It can be seen that in detecting communities, the more accurate the weights of edges detected based on the node similarity, the more precise the local algorithm will be. In fact, the algorithm has the ability to detect all the graph’s communities in a network using local information as well as identifying various roles of nodes, either being (core or outlier). Test results on both real-world and artificial networks prove that the quality of the communities which are detected by the proposed algorithm is better than the results which are achieved by other state-of-the-art algorithms in the complex networks.

Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules. We then transform the condition-specific module discovery problem into a clustering problem in multiple networks. We develop an efficient heuristic algorithm for the S pecific M odules in M ultiple N etworks ( SMMN ), which discovers the condition-specific modules by considering multiple networks. By using the artificial networks, we demonstrate that SMMN outperforms state-of-the-art methods. In breast cancer networks, stage-specific modules discovered by SMMN are more discriminative in predicting cancer stages than those obtained by other techniques. In pan-cancer networks, cancer-specific modules are more likely to associate with survival time of patients, which is critical for cancer therapy.

The assessment of sustainability provides the actual situation of a site and provides information for decision making. In the Amazon, the assessment of the sustainability benefits of using adapted analytical models. However, this model is not always accurate. In this paper we propose a methodology that uses an artificial intelligence algorithm based on artificial neural networks (ANN), as a decision support tool, which aims to classify agroecosystems with agroforestry systems and without agroforestry systems. The sites are classified into two groups: Sustainable and Not Sustainable. For data collection we carried out technical visits to the rural community of Santa Luzia, in the municipality of Tome-Acu, Para Amazon. During the research data of 28 production units were collected. Finally, the data were submitted to the ANN and the results demonstrate the high potential of using computational intelligence algorithms for the purpose of monitoring the agroforestry systems in the Amazon region.

DNA methylation is a critical epigenetic modification that plays an important role in cancers. The available algorithms fail to fully characterize epigenetic modules. To address this issue, we first characterize the epigenetic module as a group of well-connected genes in the protein interaction network and are also co-methylated based on gene methylation profiles. Then, the epigenetic module discovery problem is transformed into an optimization problem. Then, a regularized nonnegative matrix factorization algorithm for methylation modules ( RNMF-MM ) is presented, where the co-methylation constraint is treated as a regularizer. Using the artificial networks with known module structure, we demonstrate that the proposed algorithm outperforms state-of-the-art approaches in terms of accuracy. On the basis of breast cancer methylation data and protein interaction network, the RNMF-MM algorithm discovers methylation modules that are significantly more enriched by the known pathways than those obtained by other algorithms. These modules serve as biomarkers for predicting cancer stages and estimating survival time of patients. The proposed model and algorithm provide an effective way for the integrative analysis of protein interaction network and methylation data.