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Abstract Background Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms. Results In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge. Conclusion In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.

Background: Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms. Results: In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge. Conclusion: In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network. [ABSTRACT FROM AUTHOR]

[Display omitted] • TPQCI is a metric to effectively quantify the influence of CNV on genes. • TPQCI can capture both gene and functional level associations. • TPQCI use topology potential to integrate both network and CNV events information. • TPQCI-based module detection can help identify driving cytogenetic aberrations. Copy number variation (CNV) is a major type of chromosomal structural variation that play important roles in many diseases including cancers. Due to genome instability, a large number of CNV events can be detected in diseases such as cancer. Therefore, it is important to identify the functionally important CNVs in diseases, which currently still poses a challenge in genomics. One of the critical steps to solve the problem is to define the influence of CNV. In this paper, we provide a topology potential based method, TPQCI, to quantify this kind of influence by integrating statistics, gene regulatory associations, and biological function information. We used this metric to detect functionally enriched genes on genomic segments with CNV in breast cancer and multiple myeloma and discovered biological functions influenced by CNV. Our results demonstrate that, by using our proposed TPQCI metric, we can detect disease-specific genes that are influenced by CNVs. Source codes of TPQCI are provided in Github (https://github.com/usos/TPQCI). [ABSTRACT FROM AUTHOR]

In this paper, a novel energy-aware grooming-based algorithm, named energy minimization algorithm (EMA), is proposed that aims at providing the quality of service requirements in elastic optical networks in terms of network energy efficiency and requests blocking probability. To this end, EMA divides the incoming requests into high priority and low priority requests and applies fragmentation and squeezing mechanisms on the low priority traffic to alleviate the blocking probability of high priority requests. Moreover, EMA proposes a novel mechanism to select the optimal paths in the routing phase so that the network energy consumption is minimized. The simulation results reveal that the proposed EMA improves the network energy efficiency and mitigates the requests blocking probability. [ABSTRACT FROM AUTHOR]

In this paper, a reliable algorithm is proposed, which aims at providing the desired reliability, avoiding the data loss due to probable failures, and guaranteeing the energy consumption balancing among all fog nodes (FNs) in the fog-enabled Internet of Things (IoT) networks. To this end, first, a new optimization model is defined to find the best transmission power and the best transmission rate of each terminal node (TN) based on the channel conditions and the desired reliability of the network. Then, the best FN is assigned to each TN according to the topology potential between FNs and TNs, such that there exists an energy consumption balancing among all FNs. Simulation results reveal that the proposed algorithm improves the network performance in terms of reliability and loss ratio, while maintaining the energy consumption balancing among all FNs. [ABSTRACT FROM AUTHOR]

To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer linear programming model for the VNE problem, and proposed a novel two-stage virtual network embedding algorithm based on topology potential (VNE-TP). In the node embedding stage, the field theory once used for data clustering was introduced and a node embedding function designed to find the optimal physical node. In the link embedding stage, both the available bandwidth and hops of the candidate paths were considered, and a path embedding function designed to find the optimal path. Extensive simulation results show that the proposed algorithm outperforms other existing algorithms in terms of acceptance ratio and revenue to cost ratio.

Most current community detection methods for complex networks focus on partition. Community number estimation does not have the due attention it deserves, and the community number is only a by-product of community partition. In fact, knowing the community number in advance can speed up the partition process, especially for large scale and dynamic complex networks. This paper proposes a community number estimation method based on topology potential. In the topology potential field, the potential distribution of nodes shows a natural peak-valley structure, and each community corresponds to a local high potential area. The number of local maximum potential nodes is the estimated community number. Experiments on real world networks and artificial networks show that the proposed method gives very good performance in community number estimation. The more noticeable the peak-valley structure of the corresponding topology potential field is, the closer the estimated community number will be to the ground truth. Furthermore, compared with state-of-the-arts methods, our proposed method is not sensitive to the tuned parameter, and shows good efficiency. [ABSTRACT FROM AUTHOR]

In reconfigurable network environment,a major challenge is how to map virtual networks to the substrate network efficiently,network centrality and topology potential were introduced into the allocation of virtual resources.Two algorithms were proposed that consider the embedding problem with the attributes of network topology.Simulation experiments show that the proposed algorithms increase the acceptance ratio and the revenue,while decreasing the provisioning cost in the long term.

As a novel overlapping community detection theory, topology potential has inspired many methods. However, these methods ignore the mass difference between nodes, leading to inaccurate topological potential values of nodes. Moreover, additional strategies are needed to determine the community affiliation of nodes, further complicating the process of community detection. In this paper, we propose a new overlapping community detection method based on node location analysis. In the proposed method, the PageRank algorithm is used to evaluate the node mass, and the community affiliation of nodes is determined based on their positions in the inherent peak-valley structure of the topology potential field. Experimental results show that the proposed method exhibits excellent performance on artificial and real-world networks and outperforms other topology-potential-based and most non-topology-potential-based methods. [ABSTRACT FROM AUTHOR]

Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins’ topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein’s topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential. [ABSTRACT FROM PUBLISHER]

There has been considerable interest in designing algorithms for detecting community structure in real-world complex networks. A majority of these algorithms assume that communities are disjoint, placing each vertex in only one cluster. However, in nature, it is a matter of common experience that communities often overlap and members often play multiple roles in a network topology. To further investigate these properties of overlapping communities and heterogeneity within the network topology, a new method is proposed to divide networks into separate communities by spreading outward from each local important element and extracting its neighbors within the same group in each spreading operation. When compared with the state of the art, our new algorithm can not only classify different types of nodes at a more fine-grained scale successfully but also detect community structure more effectively. We also evaluate our algorithm using the standard data sets. Our results show that it performed well not only in the efficiency of algorithm, but also with a higher accuracy of partition results. [ABSTRACT FROM AUTHOR]

To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer linear programming model for the VNE problem, and proposed a novel two-stage virtual network embedding algorithm based on topology potential (VNE-TP). In the node embedding stage, the field theory once used for data clustering was introduced and a node embedding function designed to find the optimal physical node. In the link embedding stage, both the available bandwidth and hops of the candidate paths were considered, and a path embedding function designed to find the optimal path. Extensive simulation results show that the proposed algorithm outperforms other existing algorithms in terms of acceptance ratio and revenue to cost ratio. [ABSTRACT FROM AUTHOR]