Sewage flow optimization algorithm for large-scale urban sewer networks based on network community division

By considering the flow control of urban sewer networks to minimize the electricity consumption of pumping stations, a decomposition-coordination strategy for energy savings based on network community division is developed in this paper. A mathematical model characterizing the steady-state flow of urban sewer networks is first constructed, consisting of a set of algebraic equations with the structure transportation capacities captured as constraints. Since the sewer networks have no apparent natural hierarchical structure in general, it is very difficult to identify the clustered groups. A fast network division approach through calculating the betweenness of each edge is successfully applied to identify the groups and a sewer network with arbitrary configuration could be then decomposed into subnetworks. By integrating the coupling constraints of the subnetworks, the original problem is separated into N optimization subproblems in accordance with the network decomposition. Each subproblem is solved locally and the solutions to the subproblems are coordinated to form an appropriate global solution. Finally, an application to a specified large-scale sewer network is also investigated to demonstrate the validity of the proposed algorithm.

Community detection with consideration of non-topological information

In a network described by a graph, only topological structure information is considered to determine how the nodes are connected by edges. Non-topological information denotes that which cannot be determined directly from topological information. This paper shows, by a simple example where scientists in three research groups and one external group form four communities, that in some real world networks non-topological information （in this example, the research group affiliation） dominates community division. If the information has some influence on the network topological structure, the question arises as to how to find a suitable algorithm to identify the communities based only on the network topology. We show that weighted Newman algorithm may be the best choice for this example. We believe that this idea is general for real-world complex networks.

An improvement of the fast uncovering community algorithm

Community detection methods have been used in computer, sociology, physics, biology, and brain information science areas. Many methods are based on the optimization of modularity. The algorithm proposed by Blondel et al. （Blondel V D, Guillaume J L, Lambiotte R and Lefebvre E 2008 J. Star. Mech. 10 10008） is one of the most widely used methods because of its good performance, especially in the big data era. In this paper we make some improvements to this algorithm in correctness and performance. By tests we see that different node orders bring different performances and different community structures. We find some node swings in different communities that influence the performance. So we design some strategies on the sweeping order of node to reduce the computing cost made by repetition swing. We introduce a new concept of overlapping degree （OV） that shows the strength of connection between nodes. Three improvement strategies are proposed that are based on constant OV, adaptive OV, and adaptive weighted OV, respectively. Experiments on synthetic datasets and real datasets are made, showing that our improved strategies can improve the performance and correctness.

Discovering the relationship of disasters from big scholar and social media news datasets

The construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster’s evolution.Especially when dealing with the complexity and diversity of disasters,it is critical to make a further investigation on reducing the dependency of prior knowledge and supporting the comprehensive chains of disasters.This paper tries to propose a novel approach,through collecting the big scholar and social news data with disasterrelated keywords,analysing the strength of their relationships with the co-word analysis method,and constructing a complex network of all defined disaster types,in order to finally intelligently extract the unique disaster chain of a specific disaster type.Google Scholar,Baidu Scholar and Sina News search engines are employed to acquire the needed data,and the respectively obtained disaster chains are compared with each other to show the robustness of our proposed approach.The achieved disaster chains are also compared with the ones concluded from existing research methods,and the very reasonable result is demonstrated.There is a great potential to apply this novel method in disaster management domain to find more secrets about disasters.