Increasing regional competitiveness by network strategy case: The strategy process of Lahti University Network

In a network society different areas and cities are forced to compete with and against each other. The success of urban districts is partly dependent of the people`s level of know-how and the districts` capability to create, process and spread out knowledge. Although Lahti Region has been considered a declined industrial region, it is full of potential: There are a sufficient amount of inhabitants; its infrastructure is competitive; its logistical position in Finland is central; it is near the capital city, Helsinki; the nature is near in Lahti. The city has innovative enterprises in wood, metal and plastic industries. On the other hand, the lack of university leads to remote R&D-spending, low standard of education and to the fact that the flow of young, educated people is easily passing the urban district of Lahti. Although Lahti does not have a university of its own, it has been able to attract some Finnish universities to start up branch offices in Lahti. Nowadays, there are three units from different universities with nine professorships. The lack of the university emphasizes the need of local actors, like research and education organisations, companies, authorities and Lahti Region Centre of Expertise Programme to co-operate strategically with each other. One example of this co-operation is the Lahti University Centre (LUC). LUC is a network of independent university level organisations in the city of Lahti. According to the co-operation contract, it consists of the following actors: Helsinki University of Technology Lahti Center; Lappeenranta University of Technology; University of Helsinki, Palmenia Centre for Research and Continuing Education; Helsinki University, Department of Ecological and Enviromental Sciences. The basic task of the LUC is to raise the university level know-how capacity in Lahti region and serve its economic life in areas of research, development and education in order to increase regional competitiveness. Although Lahti University Center has been established it does not have a proper strategy or an action plan. The network of these independent university organisations needs a strategy which guidelines and supports their actions and is compatible with the regional innovation system. The study focuses on the strategy process of a network organisation. Research problems are: - how leadership will be decentralized in a network - the roles and commitment of actors - how to formulate a viable vision - what are the main areas of strategic co-operation The study is an action research, where researchers also be actors in the strategy process. The strategy process will be understood as a learning process of the network. Some traditional strategic analyses such as feasibility analysis and benchmarking as well as some creativity tools like vision workshop will be tested. Leadership will be decentralized among all the actors in order to get the full engagement of the actors. The network strategy of the Lahti University Center gives information and serves as a possible benchmarking partner for other similar network organisations in Europe. This case will be one part of the researchers` study project to develop a model for the strategic planning of the network.

Integrating fluctuations into distribution of resources in transportation networks

We propose a resource distribution strategy to reduce the average travel time in a transportation network given a fixed generation rate. Suppose that there are essential resources to avoid congestion in the network as well as some extra resources. The strategy distributes the essential resources by the average loads on the vertices and integrates the fluctuations of the instantaneous loads into the distribution of the extra resources. The fluctuations are calculated with the assumption of unlimited resources, where the calculation is incorporated into the calculation of the average loads without adding to the time complexity. Simulation results show that the fluctuation-integrated strategy provides shorter average travel time than a previous distribution strategy while keeping similar robustness. The strategy is especially beneficial when the extra resources are scarce and the network is heterogeneous and lowly loaded.Comment: 14 pages, 4 figure

Infection Spreading and Source Identification: A Hide and Seek Game

The goal of an infection source node (e.g., a rumor or computer virus source) in a network is to spread its infection to as many nodes as possible, while remaining hidden from the network administrator. On the other hand, the network administrator aims to identify the source node based on knowledge of which nodes have been infected. We model the infection spreading and source identification problem as a strategic game, where the infection source and the network administrator are the two players. As the Jordan center estimator is a minimax source estimator that has been shown to be robust in recent works, we assume that the network administrator utilizes a source estimation strategy that can probe any nodes within a given radius of the Jordan center. Given any estimation strategy, we design a best-response infection strategy for the source. Given any infection strategy, we design a best-response estimation strategy for the network administrator. We derive conditions under which a Nash equilibrium of the strategic game exists. Simulations in both synthetic and real-world networks demonstrate that our proposed infection strategy infects more nodes while maintaining the same safety margin between the true source node and the Jordan center source estimator

Efficiency of attack strategies on complex model and real-world networks

We investigated the efficiency of attack strategies to network nodes when targeting several complex model and real-world networks. We tested 5 attack strategies, 3 of which were introduced in this work for the first time, to attack 3 model (Erdos and Renyi, Barabasi and Albert preferential attachment network, and scale-free network configuration models) and 3 real networks (Gnutella peer-to-peer network, email network of the University of Rovira i Virgili, and immunoglobulin interaction network). Nodes were removed sequentially according to the importance criterion defined by the attack strategy. We used the size of the largest connected component (LCC) as a measure of network damage. We found that the efficiency of attack strategies (fraction of nodes to be deleted for a given reduction of LCC size) depends on the topology of the network, although attacks based on the number of connections of a node and betweenness centrality were often the most efficient strategies. Sequential deletion of nodes in decreasing order of betweenness centrality was the most efficient attack strategy when targeting real-world networks. In particular for networks with power-law degree distribution, we observed that most efficient strategy change during the sequential removal of nodes.Comment: 18 pages, 4 figure

Using synchronism of chaos for adaptive learning of network topology

In this paper we consider networks of dynamical systems that evolve in synchrony and investigate how dynamical information from the synchronization dynamics can be effectively used to learn the network topology, i.e., identify the time evolution of the couplings between the network nodes. To this aim, we present an adaptive strategy that, based on a potential that the network systems seek to minimize in order to maintain synchronization, can be successfully applied to identify the time evolution of the network from limited information. This strategy takes advantage of the properties of synchronism of chaos and of the presence of different communication delays over the network links. As a motivating example we consider a network of sensors surveying an area, in which information regarding the time evolution of the network connections can be used, e.g., to detect changes taking place within the area. We propose two different setups for our strategy. In the first one, synchronization has to be achieved at each node (as well as the identification of the couplings over the network links), based solely on a single scalar signal representing a superposition of signals from the other nodes in the network. In the second one, we incorporate an additional node, termed the maestro, having the function of maintaining network synchronization. We will see that when such an arrangement is realized, it will become possible to effectively identify the time evolution of networks that are much larger than would be possible in the absence of a maestro.Comment: 22 pages, 12 figures, accepted for publication on Physical Review