Patterns of interest change in stack overflow.

Stack Overflow is currently the largest programming related question and answer community, containing multiple programming areas. The change of user's interest is the micro-representation of the intersection of macro-knowledge and has been widely studied in scientific fields, such as literature data sets. However, there is still very little research for the general public, such as the question and answer community. Therefore, we analyze the interest changes of 2,307,720 users in Stack Overflow in this work. Specifically, we classify the tag network in the community, vectorize the topic of questions to quantify the user's interest change patterns. Results show that the change pattern of user interest has the characteristic of a power-law distribution, which is different from the exponential distribution of scientists' interest change, but they are all affected by three features, heterogeneity, recency and proximity. Furthermore, the relationship between users' reputations and interest changes is negatively correlated, suggesting the importance of concentration, i.e., those who focus on specific areas are more likely to gain a higher reputation. In general, our work is a supplement to the public interest changes in science, and it can also help community managers better design recommendation algorithms and promote the healthy development of communities.

Water Affects Morphogenesis of Growing Aquatic Plant Leaves.

Lotus leaves floating on water usually experience short-wavelength edge wrinkling that decays toward the center, while the leaves growing above water normally morph into a global bending cone shape with long rippled waves near the edge. Observations suggest that the underlying water (liquid substrate) significantly affects the morphogenesis of leaves. To understand the biophysical mechanism under such phenomena, we develop mathematical models that can effectively account for inhomogeneous differential growth of floating and freestanding leaves to quantitatively predict formation and evolution of their morphology. We find, both theoretically and experimentally, that the short-wavelength buckled configuration is energetically favorable for growing membranes lying on liquid, while the global buckling shape is more preferable for suspended ones. Other influencing factors such as the stem or vein, heterogeneity, and dimension are also investigated. Our results provide a fundamental insight into a variety of plant morphogenesis affected by water foundation and suggest that such surface instabilities can be harnessed for morphology control of biomimetic deployable structures using substrate or edge actuation.

Information filtering by smart nodes in random networks.

Diffusion of information in social networks has drawn extensive attention from various scientific communities, with many contagion models proposed to explain related phenomena. In this paper, we present a simple contagion mechanism, in which a node will change its state immediately if it is exposed to the diffusive information. By considering two types of nodes (smart and normal) and two kinds of information (true and false), we study analytically and numerically how smart nodes influence the spreading of information, which leads to information filtering. We find that for randomly distributed smart nodes, the spreading dynamics over random networks with Poisson degree distribution and power-law degree distribution (with relatively small cutoffs) can both be described by the same approximate mean-field equation. Increasing the heterogeneity of the network may elicit more deviations, but not much. Moreover, we demonstrate that more smart nodes make the filtering effect on a random network better. Finally, we study the efficacy of different strategies of selecting smart nodes for information filtering.

Social Synchrony on Complex Networks.

Social synchrony (SS) is an emergent phenomenon in human society. People often mimic others which, over time, can result in large groups behaving similarly. Drawing from prior empirical studies of SS in online communities, here we propose a discrete network model of SS based on four attributes: 1) depth of action; 2) breadth of impact, i.e., a large number of actions are performed with a large group of people involved; 3) heterogeneity of role, i.e., people of higher degree play more important roles; and 4) lastly, emergence of phenomenon, i.e., it is far from random. We analyze our model both analytically and with simulations, and find good agreement between the two. We find this model can well explain the four characters of SS, and thus hope it can help researchers better understand human collective behavior.

Temporal motifs reveal collaboration patterns in online task-oriented networks.

Real networks feature layers of interactions and complexity. In them, different types of nodes can interact with each other via a variety of events. Examples of this complexity are task-oriented social networks (TOSNs), where teams of people share tasks towards creating a quality artifact, such as academic research papers or software development in commercial or open source environments. Accomplishing those tasks involves both work, e.g., writing the papers or code, and communication, to discuss and coordinate. Taking into account the different types of activities and how they alternate over time can result in much more precise understanding of the TOSNs behaviors and outcomes. That calls for modeling techniques that can accommodate both node and link heterogeneity as well as temporal change. In this paper, we report on methodology for finding temporal motifs in TOSNs, limited to a system of two people and an artifact. We apply the methods to publicly available data of TOSNs from 31 Open Source Software projects. We find that these temporal motifs are enriched in the observed data. When applied to software development outcome, temporal motifs reveal a distinct dependency between collaboration and communication in the code writing process. Moreover, we show that models based on temporal motifs can be used to more precisely relate both individual developer centrality and team cohesion to programmer productivity than models based on aggregated TOSNs.

Synchronization transition in networked chaotic oscillators: the viewpoint from partial synchronization.

Synchronization transition in networks of nonlocally coupled chaotic oscillators is investigated. It is found that in reaching the state of global synchronization the networks can stay in various states of partial synchronization. The stability of the partial synchronization states is analyzed by the method of eigenvalue analysis, in which the important roles of the network topological symmetry on synchronization transition are identified. Moreover, for networks possessing multiple topological symmetries, it is found that the synchronization transition can be divided into different stages, with each stage characterized by a unique synchronous pattern of the oscillators. Synchronization transitions in networks of nonsymmetric topology and nonidentical oscillators are also investigated, where the partial synchronization states, although unstable, are found to be still playing important roles in the transitions.

Synchronization and quorum sensing in an ensemble of indirectly coupled chaotic oscillators.

The fact that the elements in some realistic systems are influenced by each other indirectly through a common environment has stimulated a new surge of studies on the collective behavior of coupled oscillators. Most of the previous studies, however, consider only the case of coupled periodic oscillators, and it remains unknown whether and to what extent the findings can be applied to the case of coupled chaotic oscillators. Here, using the population density and coupling strength as the tuning parameters, we explore the synchronization and quorum sensing behaviors in an ensemble of chaotic oscillators coupled through a common medium, in which some interesting phenomena are observed, including the appearance of the phase synchronization in the process of progressive synchronization, the various periodic oscillations close to the quorum sensing transition, and the crossover of the critical population density at the transition. These phenomena, which have not been reported for indirectly coupled periodic oscillators, reveal a corner of the rich dynamics inherent in indirectly coupled chaotic oscillators, and are believed to have important implications to the performance and functionality of some realistic systems.

Synchronous patterns in complex systems.

When a complex network is slightly desynchronized, a few of the network nodes will be escaping from the uniform synchronization background frequently with a random fashion, leading to the intermittent network synchronization. Here, based on the eigenvectors of the network coupling matrix, we propose a new method which is able to figure out the unstable nodes in the general case of desynchronized complex networks. Moreover, with this method, we are also able to regulate the seemingly random network dynamics into stable and visible synchronous patterns. The efficiency of this method is verified by a variety of network models, including varying the network structures, the node local dynamics, and the desynchronization types. Our studies show that, even for the complex network systems, synchronous patterns can still be identified and characterized.

Network growth under the constraint of synchronization stability.

While it is well recognized that realistic networks are typically growing with time, the dynamical features of their growing processes remain to be explored. In the present paper, incorporating the requirement of synchronization stability into the conventional models of network growth, we will investigate how the growing process of a complex network is influenced by, and also will influence, the network collective dynamics. Our study shows that, constrained by the synchronization stability, the network will be growing in a selective and dynamical fashion. In particular, we find that the chance for a new node to be accepted by the growing network could have a large variation, i.e., it follows roughly a power-law distribution. Furthermore, we find that, with the dynamical growth, the network is always developed into structures of clear scale-free features, despite the form of the link attachment (preferential or random). The dynamical properties of network growth are studied using the method of eigenvalue analysis, and they are verified by direct simulations of coupled chaotic oscillators. Our study implies that, driven by the network collective dynamics, network growth could also be highly dynamical.