Network ensemble clustering using latent roles

We present a clustering method for collections of graphs based on the assumptions that graphs in the same cluster have a similar role structure and that the respective roles can be founded on implicit vertex types. Given a network ensemble (a collection of attributed graphs with some substantive commonality), we start by partitioning the set of all vertices based on attribute similarity. Projection of each graph onto the resulting vertex types yields feature vectors of equal dimensionality, irrespective of the original graph sizes. These feature vectors are then subjected to standard clustering methods. This approach is motivated by social network concepts, and we demonstrate its utility on an ensemble of personal networks of migrants, where we extract structurally similar groups and show their resemblance to predicted acculturation strategies.     

Dominating sets in social network graphs

This paper presents an approach to the study of the structure of social networks using dominating sets of graphs. A method is presented for partitioning the vertices of a graph using dominating vertices. For certain classes of graphs this is helpful in determining the underlying structure of the corresponding social network. An extension of this technique provides a method of studying the structure of directed graphs and directed social networks. Minimum dominating sets are related to statuses and structurally equivalent sets.     

Geometric aspects of 2-walk-regular graphs

A t-walk-regular graph is a graph for which the number of walks of given length between two vertices depends only on the distance between these two vertices, as long as this distance is at most t. Such graphs generalize distance-regular graphs and t-arc-transitive graphs. In this paper, we will focus on 1- and in particular 2-walk-regular graphs, and study analogues of certain results that are important for distance-regular graphs. We will generalize Delsarte?s clique bound to 1-walk-regular graphs, Godsil?s multiplicity bound and Terwilliger?s analysis of the local structure to 2-walk-regular graphs. We will show that 2-walk-regular graphs have a much richer combinatorial structure than 1-walk-regular graphs, for example by proving that there are finitely many non-geometric 2-walk-regular graphs with given smallest eigenvalue and given diameter (a geometric graph is the point graph of a special partial linear space); a result that is analogous to a result on distance-regular graphs. Such a result does not hold for 1-walk-regular graphs, as our construction methods will show.     

The Zero-Divisor Graph Associated to a Semigroup

The zero-divisor graph of a commutative semigroup with zero is the graph whose vertices are the nonzero zero-divisors of the semigroup, with two distinct vertices adjacent if the product of the corresponding elements is zero. New criteria to identify zero-divisor graphs are derived using both graph-theoretic and algebraic methods. We find the lowest bound on the number of edges necessary to guarantee a graph is a zero-divisor graph. In addition, the removal or addition of vertices to a zero-divisor graph is investigated by using equivalence relations and quotient sets. We also prove necessary and sufficient conditions for determining when regular graphs and complete graphs with more than two triangles attached are zero-divisor graphs. Lastly, we classify several graph structures that satisfy all known necessary conditions but are not zero-divisor graphs.     

Bayesian selection of graphical regulatory models

We define a new class of coloured graphical models, called regulatory graphs. These graphs have their own distinctive formal semantics and can directly represent typical qualitative hypotheses about regulatory processes like those described by various biological mechanisms. They admit an embellishment into classes of probabilistic statistical models and so standard Bayesian methods of model selection can be used to choose promising candidate explanations of regulation. Regulation is modelled by the existence of a deterministic relationship between the longitudinal series of observations labelled by the receiving vertex and the donating one. This class contains longitudinal cluster models as a degenerate graph. Edge colours directly distinguish important features of the mechanism like inhibition and excitation and graphs are often cyclic. With appropriate distributional assumptions, because the regulatory relationships map onto each other through a group structure, it is possible to define a conditional conjugate analysis. This means that even when the model space is huge it is nevertheless feasible, using a Bayesian MAP search, to a discover regulatory network with a high Bayes Factor score. We also show that, like the class of Bayesian Networks, regulatory graphs also admit a formal but distinctive causal algebra. The topology of the graph then represents collections of hypotheses about the predicted effect of controlling the process by tearing out message passers or forcing them to transmit certain signals. We illustrate our methods on a microarray experiment measuring the expression of thousands of genes as a longitudinal series where the scientific interest lies in the circadian regulation of these plants.     

Fuzzy graphs modelling for HazMat telegeomonitoring

Telegeomonitoring system development combines two heterogeneous technologies: the geographical information systems technology (GIS) and telecommunications technology. In this paper, we give the system components for telegeomonitoring transportation of hazardous materials. The telegeomonitoring system uses GIS to capture civil infrastructure (urban network, land use, industries, etc.) and decision support systems technology to allow risks analysis and evaluate routing strategies that minimize transportation risk. Routing algorithms are to this effect adapted to graphs of the fuzzy risk. A new algebraic structure is proposed to solve a path-finding problem in a fuzzy graph. This algebraic structure is adapted precisely to solve the problem of the K-best fuzzy shortest paths. The approach that we proposed, consists of defining generic structures of operator’s traversal problem in fuzzy graphs. The principal contribution of our approach is to build adequate structures of path algebra to solve the problem of graph traversal in a fuzzy graph without negative circuits. Foundations of the system studied in this work will be able to be transposed to other fields of transportation.     

Complexity of networks I: The set‐complexity of binary graphs

The balance between symmetry and randomness as a property of networks can be viewed as a kind of “complexity.” We use here our previously defined “set complexity” measure (Galas et al., IEEE Trans Inf Theory 2010, 56), which was used to approach the problem of defining biological information, in the mathematical analysis of networks. This information theoretic measure is used to explore the complexity of binary, undirected graphs. The complexities, Ψ, of some specific classes of graphs can be calculated in closed form. Some simple graphs have a complexity value of zero, but graphs with significant values of Ψ are rare. We find that the most complex of the simple graphs are the complete bipartite graphs (CBGs). In this simple case, the complexity, Ψ, is a strong function of the size of the two node sets in these graphs. We find the maximum Ψ binary graphs as well. These graphs are distinct from, but similar to CBGs. Finally, we explore directed and stochastic processes for growing graphs (hill‐climbing and random duplication, respectively) and find that node duplication and partial node duplication conserve interesting graph properties. Partial duplication can grow extremely complex graphs, while full node duplication cannot do so. By examining the eigenvalue spectrum of the graph Laplacian we characterize the symmetry of the graphs and demonstrate that, in general, breaking specific symmetries of the binary graphs increases the set‐based complexity, Ψ. The implications of these results for more complex, multiparameter graphs, and for physical and biological networks and the processes of network evolution are discussed. © 2011 Wiley Periodicals, Inc. Complexity, 17,51–64, 2011     

Intuitionistic fuzzy soft graphs with applications

We present different types of new concepts, including intuitionistic fuzzy soft graphs, complete intuitionistic fuzzy soft graph, strong intuitionistic fuzzy soft graph and self complement of intuitionistic fuzzy soft graph. We describe various methods of their construction, and investigate some of their related properties. We also discuss applications of intuitionistic fuzzy soft graphs in communication network and decision making.     

Multiresolution Network Models

Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize a global partition of the graph, whereas projection-based approaches (e.g., the latent space model in the statistics literature) represent in rich detail the roles of individuals. Many pertinent questions in sociology and economics, however, span multiple scales of analysis. Further, many questions involve comparisons across disconnected graphs that will, inevitably be of different sizes, either due to missing data or the inherent heterogeneity in real-world networks. We propose a class of network models that represent network structure on multiple scales and facilitate comparison across graphs with different numbers of individuals. These models differentially invest modeling effort within subgraphs of high density, often termed communities, while maintaining a parsimonious structure between said subgraphs. We show that our model class is projective, highlighting an ongoing discussion in the social network modeling literature on the dependence of inference paradigms on the size of the observed graph. We illustrate the utility of our method using data on household relations from Karnataka, India. Supplementary material for this article is available online.     

Computational Algorithms for Censored-Data Problems Using Intersection Graphs

This article presents methods for finding the nonparametric maximum likelihood estimate (NPMLE) of the distribution function of time-to-event data. The basic approach is to use graph theory (in particular intersection graphs) to simplify the problem. Censored data can be represented in terms of their intersection graph. Existing combinatorial algorithms can be used to find the important structures, namely the maximal cliques. When viewed in this framework there is no fundamental difference between right censoring, interval censoring, double censoring, or current status data and hence the algorithms apply to all types of data. These algorithms can be extended to deal with bivariate data and indeed there are no fundamental problems extending the methods to higher dimensional data. Finally this article shows how to obtain the NPMLE using convex optimization methods and methods for mixing distributions. The implementation of these methods is greatly simplified through the graph-theoretic representation of the data.     

电阻-距离谱的若干结果

对连通图$G$的顶点$u$和$v$, $u$与$v$在$G$中的电阻距离$r_G(u,v)$等于相邻顶点之间的电阻为单位电阻的$G$对应的电网中$u$与$v$之间的等效电阻. 图$G$的电阻-距离特征值是$G$的电阻-距离矩阵$R(G)=(r_G(u,v))_{u,v\in V(G)}$的特征值. 我们分别确定了不同于完全图与完全图删去一条边后得到的图及给定割边数目的使得最大电阻-距离特征值取得最小值的唯一的连通图, 还讨论了最小电阻-距离特征值的性质.     

On a parallel genetic–tabu search based algorithm for solving the graph colouring problem

In this paper, we are interested in a particular combinatorial optimisation problem (COP), namely the graph colouring problem (GCP). To solve the GCP, we present a parallel approach adopting an efficient strategy. A brief survey on known methods for solving the GCP enables us to justify our approach which is based on a hybrid method, starting from a set of solutions initialized by the so-called RLF colouring method and combining both a genetic algorithm and the tabu search. A parallelising strategy is then applied. The performances of our method were evaluated through a series of experimentations achieved on an IBM SP2 multiprocessor. The processed graphs were chosen from two benchmark sets. The first, taken from the Internet, involves graphs whose chromatic numbers are known and the second involves random generated graphs. The analysis of the results proves the interest of our approach.     

Activity graphs: A language for flexible consultation systems

An intelligent decision system (IDS) uses artificial intelligence principles to deliver automated, interactive decision analysis (DA) consultations. Network methods adapted from operations research underlie two key IDS components: influence diagrams and activity graphs. Influence diagrams, which are familiar to DA researchers and practitioners, represent decision problems inevent space. Activity graphs, which are introduced in this paper, represent processes inaction space. While activity graphs can represent any process, we use them as a knowledge-engineering and programming language to represent the process knowledge of skilled decision analysts in the context of a specific class of decisions. This paper defines activity graphs as an extension of directed AND-OR graphs. Anactivity tree is a directed AND-OR tree consisting of nodes, which may contain activities (small computer programs) and connectors that establish logical relationships among nodes and define logical resolution agendas. Anactivity graph is a directed, multiply connected network of activity trees. Activity graphs may involve recursion. Development of the activity graph language is motivated by our desire to enable professional decision analysts — or other experts — with limited advanced programming experience to design and build consultation systems that combine the guidance offered by protocol systems with the flexibility and generality of transaction systems. This paper defines the activity graph language in detail. A simple example illustrates key concepts. The paper also discusses our experience using a computer system that implements activity graphs for developing commercial IDSs.     

Note on 3-choosability of planar graphs with maximum degree 4

Deciding whether a planar graph (even of maximum degree 4) is 3-colorable is NP-complete. Determining subclasses of planar graphs being 3-colorable has a long history, but since Grötzsch’s result that triangle-free planar graphs are such, most of the effort was focused to solving Havel’s and Steinberg’s conjectures. In this paper, we prove that every planar graph obtained as a subgraph of the medial graph of any bipartite plane graph is 3-choosable. These graphs are allowed to have close triangles (even incident), and have no short cycles forbidden, hence representing an entirely different class than the graphs inferred by the above mentioned conjectures.     

Locally s‐distance transitive graphs

We give a unified approach to analyzing, for each positive integer s, a class of finite connected graphs that contains all the distance transitive graphs as well as the locally s‐arc transitive graphs of diameter at least s. A graph is in the class if it is connected and if, for each vertex v, the subgroup of automorphisms fixing v acts transitively on the set of vertices at distance i from v, for each i from 1 to s. We prove that this class is closed under forming normal quotients. Several graphs in the class are designated as degenerate, and a nondegenerate graph in the class is called basic if all its nontrivial normal quotients are degenerate. We prove that, for s≥2, a nondegenerate, nonbasic graph in the class is either a complete multipartite graph or a normal cover of a basic graph. We prove further that, apart from the complete bipartite graphs, each basic graph admits a faithful quasiprimitive action on each of its (1 or 2) vertex‐orbits or a biquasiprimitive action. These results invite detailed additional analysis of the basic graphs using the theory of quasiprimitive permutation groups. © 2011 Wiley Periodicals, Inc. J Graph Theory 69:176‐197, 2012     

Multiple-retrieval case-based reasoning for course timetabling problems

The structured representation of cases by attribute graphs in a case-based reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organized as a decision tree and the retrieval process chooses those cases that are sub-attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependent upon problem-specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialization method for local search methods such as hill climbing, tabu search and simulated annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialization method for these approaches.     

Fast computation of bounds for two-terminal network reliability

In this paper, an algorithm for the fast computation of network reliability bounds is proposed. The evaluation of the network reliability is an intractable problem for very large networks, and hence approximate solutions based on reliability bounds have assumed importance. The proposed bounds computation algorithm is based on an efficient BDD representation of the reliability graph model and a novel search technique to find important minpaths/mincuts to quickly reduce the gap between the reliability upper and lower bounds. Furthermore, our algorithm allows the control of the gap between the two bounds by controlling the overall execution time. Therefore, a trade-off between prediction accuracy and computational resources can be easily made in our approach. The numerical results are presented for large real example reliability graphs to show the efficacy of our approach.