We propose a routing strategy to improve the transportation efficiency on complex networks. Instead of using the routing strategy for shortest path, we give a generalized routing algorithm to find the so-called efficient path, which considers the possible congestion in the nodes along actual paths. Since the nodes with the largest degree are very susceptible to traffic congestion, an effective way to improve traffic and control congestion, as our strategy, can be redistributing traffic load in central nodes to other noncentral nodes. Simulation results indicate that the network capability in processing traffic is improved more than 10 times by optimizing the efficient path, which is in good agreement with the analysis.

For most technical networks, the interplay of dynamics, traffic, and topology is assumed crucial to their evolution. In this Letter, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight, and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r, and degree-strength correlation are all consistent with empirical evidence.

We examined relationships among the use of therapist directives, client implementation of directives, and outcome for 43 Chinese therapists and their 96 Chinese clients at a university counseling center in mid-China. The results showed that most directives reported by both therapists and clients asked clients to act on or think about intrapersonal or interpersonal issues. Chinese therapists reported giving fewer, but clients reported receiving a similar number of directives than was found in Scheel et al.'s (1999) American sample. Client-rated fit, difficulty, and therapist influence did not predict client implementation directly, nor did implementation predict client-rated outcome directly. Instead, quantity and acceptability of directives interacted in influencing client implementation and use of directives facilitated client-rated outcome through strengthening working alliance.

For most networks, the connection between two nodes is the result of their mutual affinity and attachment. In this paper, we propose a mutual selection model to characterize the weighted networks. By introducing a general mechanism of mutual selection, the model can produce powerlaw distributions of degree, weight and strength, as confirmed in many real networks. Moreover, we also obtained the nontrivial clustering coefficient C, degree assortativity coefficient r and degreestrength correlation, depending on a model parameter m. These results are supported by present empirical evidences. Studying the degree-dependent average clustering coefficient C(k) and the degree-dependent average nearest neighbors' degree knn(k) also provide us with a better description of the hierarchies and organizational architecture of weighted networks.

No abstract