Summary The graph isomorphism problem is to determine two finite graphs that are isomorphic which is not known with a polynomial‐time solution. This paper solves the simple undirected graph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial‐time solution to check if two simple undirected graphs are isomorphic or not. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we propose a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem, and Fundamental Theorem of Arithmetic. For each of two n ‐ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.

The dynamics and characteristics behind intelligent cognitive systems lie at the heart of understanding, and devising, successful solutions to a variety of multiagent problems. Despite the extant literature on collective intelligence, important questions like “how does the effectiveness of a collective compare to its isolated members?” and “are there some general rules or properties shaping the spread of intelligence across various cognitive systems and environments?” remain somewhat of a mystery. In this paper we develop the idea of collective intelligence by giving some insight into a range of factors hindering and influencing the effectiveness of interactive cognitive systems. We measure the influence of each examined factor on intelligence independently, and empirically show that collective intelligence is a function of them all simultaneously. We further investigate how the organisational structure of equally sized groups shapes their effectiveness. The outcome is fundamental to the understanding and prediction of the collective performance of multiagent systems, and for quantifying the emergence of intelligence over different environmental settings.

Pluralistic ignorance (PI) is a common phenomenon, observed in many social settings. It occurs when the majority of a group become non-believer conformist, but mistakenly perceive others to be true conformist. PI takes many forms and leads to a wide variety of social problems, from binge drinking to repressive political regimes and ideologies. Although discussed extensively in the literature, the nature of the problem makes experimental study impractical and it has been virtually ignored in modeling and simulation studies. In this study, we present a simulation model of conditions that lead to PI in social networks. We use it to show that PI can emerge from interpersonal interactions and to confirm some well-known hypotheses about PI.

The increasing use of location-based services has raised many issues of decision support and resource allocation. A crucial problem is how to solve queries of Group k-Nearest Neighbour (GkNN). A typical example of a GkNN query is finding one or many nearest meeting places for a group of people. Existing methods mostly rely on a centralised base station. However, mobile P2P systems offer many benefits, including self-organization, fault-tolerance and load-balancing. In this study, we propose and evaluate a novel P2P algorithm focusing on GkNN queries, in which mobile query objects and static objects of interest are of two different categories. The algorithm is evaluated in the MiXiM simulation framework with both real and synthetic datasets. The results show the practical feasibility of the P2P approach for solving GkNN queries for mobile networks.

The increasing use of mobile communications has raised many issues of decision support and resource allocation. A crucial problem is how to solve queries of Reverse Nearest Neighbor (RNN). An RNN query returns all objects that consider the query object as their nearest neighbor. Existing methods mostly rely on a centralized base station. However, mobile P2P systems offer many benefits, including self-organization, fault-tolerance and load-balancing. In this study, we propose two P2P algorithms focusing on bichromatic RNN queries, in which mobile query objects and static objects of interest are of two different categories, based on a boundary polygon around the mobile query object. The Exhaustive Search Algorithm makes use of all information from the peers to aim at high accuracy rate while the Optimized Search Algorithm reduces the number of queried peers. The algorithms are evaluated in MiXiM simulation framework with a real dataset. The results show the practical feasibility of the P2P approach in solving bichromatic RNN queries for mobile networks.

Wireless sensor networks are an important new technology for remote monitoring. How to organize the nodes in a network remains a core practical problem. Node clustering is the most popular technique to increase the energy efficiency of a wireless sensor network, but the number of clusters greatly influences two performance metrics - data reliability and energy efficiency. Present methods treat these two metrics separately. To address this gap, we here introduce a joint optimal clustering technique (JOC), which optimizes both the number of clusters and the clustering process by considering intra and inter-cluster communication cost, link quality and traffic congestion so that both reliability and energy efficiency are maximized. We used simulation to compare JOC with HEED, the most popular of current clustering techniques. The results show that JOC improves network lifetime and reliability significantly.

Microblogging services, such as Twitter, generate huge volumes of data reflecting the current zeitgeist. As such they are of enormous potential value to studies ranging from data mining to social anthropology. To realize the potential, this study investigates improvements of algorithms specifically tailored for the discovery of latent socio-demographic patterns in Twitter metadata. These newly improved hybrid algorithms improve on existing ones in terms of speed and scalability (from thousands of records to millions). Testing on a real-world Twitter data set (~7.4 million messages) reveals emergent patterns in global day-to-day Twitter activity. The results demonstrate novel insight when applied to real-world Twitter data, practical large-scale applications of the methods, and suggest potential areas of future research.

Social media have rapidly become one of the principal venues for personal and public communication. This makes them rich sources of information about real-world events. As a case study, we used Twitter metadata to investigate social dimensions of the 2011 London riots. The results showed that Twitter-based commentary and participation in the London Riots are closely linked to the real-world manifestation of the riots (e.g. in terms of geographic presence). Twitter metadata on users and their messages during the riots can be used to generate useful inferences which allows us to gain a better insight into intents, information-sharing behavior, and demographics of both the rioters and observers of the riots. Pattern recognition approaches can be used to further reveal latent properties from the acquired inferences.

Complex target tracking applications require active sensor nodes to collaboratively track multiple moving targets, which can balance the trade-off between the quality of tracking and network's lifetime. In this paper, we develop a distributed sensor-selection protocol (DSSP) to activate dynamic number of sensors based on the cost metrics. Cost metrics contains energy-aware leadership cost and eagerness-based tracking cost; which selects sensors with higher energy resources and information utilities. DSSP enables an even distribution of energy consumption among the nodes to prolong the network lifetime. Our results show that the proposed scheme can significantly improve the network lifetime while maintaining the high tracking accuracy as compared to the other schemes.

Efficient target tracking applications require active sensor nodes to track a cluster of moving targets. Clustering could lead to significant cost improvement as compared to tracking individual targets. This paper presents accurate clustering of targets for both coherent and incoherent movement patterns. We propose a novel clustering algorithm that utilises an implicit dynamic time frame to assess the relational history of targets in creating a weighted graph of connected components. The proposed algorithm employs key features of localisation algorithms in target tracking, namely, estimated current and predicted locations to determine the relational directions and distances of moving targets. Our simulation results show a significant improvement on the clustering accuracy and computation time by dynamically adjusting the history-window size and predicting the relationships among targets.

One of the major challenges in the field of evolutionary algorithms (EAs) is to characterise which kinds of problems are easy and which are not. Researchers have been attracted to predict the behaviour of EAs in different domains. We introduce fitness landscape networks (FLNs) that are formed using operators satisfying specific conditions and define a new predictive measure that we call motif difficulty (MD) for comparison-based EAs. Because it is impractical to exhaustively search the whole network, we propose a sampling technique for calculating an approximate MD measure. Extensive experiments on binary search spaces are conducted to show both the advantages and limitations of MD. Multidimensional knapsack problems (MKPs) are also used to validate the performance of approximate MD on FLNs with different topologies. The effect of two representations, namely binary and permutation, on the difficulty of MKPs is analysed.

Understanding complex networks in the real world is a nontrivial task. In the study of community structures we normally encounter several examples of these networks, which makes any statistical inferencing a challenging endeavor. Researchers resort to computer-generated networks that resemble networks encountered in the real world as a means to generate many networks with different sizes, while maintaining the real-world characteristics of interest. The generation of networks that resemble the real world turns out in itself to be a complex search problem. We present a new rewiring algorithm for the generation of networks with unique characteristics that combine the scale-free effects and community structures encountered in the real world. The algorithm is inspired by social interactions in the real world, whereby people tend to connect locally while occasionally they connect globally. This local-global coupling turns out to be a powerful characteristics that is required for our proposed rewiring algorithm to generate networks with community structures, power law distributions both in degree and in community size, positive assortative mixing by degree, and the rich-club phenomenon.

Both separated and overlapping communities are useful to analyze real networks in different situations. However, to the best of our knowledge, existing community detection methods based on Evolutionary Algorithms (EAs) can detect separate communities only. This is because it is difficult to represent overlapping communities in ways that are suitable for EAs. In this paper, we first design a representation method that can represent each individual as both separated and overlapping communities without assigning the number of communities in advance. We then design three objective functions to guide the evolutionary process in different conditions. Finally, based on the designed representation and objective functions, we propose a multiobjective evolutionary algorithm to solve CDPs (MEA_CDPs) under the framework of NSGA-II. In the experiments, 4 well-known real-life benchmark networks are used to validate the performance of MEA_CDPs, and the results shown that MEA_CDPs not only can find high quality communities, but also can detect both separated and overlapping communities at the same time, and present multiple types of communities. Moreover, the overlapping nodes identified by MEA_CDPs are really ambiguous according to their edge distributes in different communities. This illustrates the effectiveness of the objective functions we designed.

Social order and unity require consensus among individuals about cooperation and other issues. Boolean network models (BN) help to explain the role played by peer interactions in the emergence of consensus. BN models represent a society as a network in which individuals are the nodes (with two states, e.g. agree/disagree) and social relationships are the edges. BN models highlight the influence of peer interactions on social cooperation, in contrast to models, such as prisoner's dilemma, that focus on individual strategies. In BN models, the behavior that emerges from peer interactions differs in subtle, but important ways from equivalent mathematical models (e.g. Markov, dynamic systems). Despite their simplicity, BN models provide potentially important insights about many social issues. They confirm that there is an upper limit to the size of groups within which peer interactions can create and maintain consensus. In large social groups, a combination of peer interaction and enforcement is needed to achieve consensus. Social consensus is brittle in the face of global influences, such as mass media, with the peer network at first impeding the spread of alternative views, then accelerating them once a critical point is passed. BN models are sensitive both to the network topology, and to the degrees of influence associated with peer-peer connections

Biomolecular studies point increasingly to the importance of modularity in the organization of the genome. Processes such as the maintenance of metabolism are controlled by suites of genes that act as distinct, self-contained units, or modules. One effect is to promote stability of inherited characters. Despite the obvious importance of genetic modules, the mechanisms by which they form and persist are not understood. One clue is that functionally related genes tend to cluster together. Here we show that genetic translocation, recombination, and natural selection play a central role in this process. We distill the question of emerging genetic modularity into three simulation experiments that show: (1) a tendency, under natural selection, for essential genes to co-locate on the same chromosome and to settle in fixed loci; (2) that genes associated with a particular function tend to form functional clusters; and (3) that genes within a functional cluster tend to become arranged in transcription order. The results also imply that high proportions of junk DNA are essential to the process.

Correct classification and prediction of tumor cells is essential for successful diagnosis and reliable future treatment. However, it is very challenging to distinguish between tumor classes using microarray with thousands of gene expressions. Removing irrelevant genes is very helpful for us to learn the relationship between genes and tumors. In this paper we have used two methods: multivariate permutation test (MPT) and significant analysis of microarray (SAM) to select significant genes for feature selection. Using those selected features, we applied support vector machine, (SVM) with polynomial, radial and linear kernels, to predict the class of testing data. Our result shows that all the samples are classified correctly. We have achieved 100% accuracy in classification among all the samples with polynomial kernel of SVM while Liner kernel shows no misclassification among BRCA1-BRCA2 and BRCA1-sporadic.

This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated.