scholarly journals SPECTRAL METHODS CLUSTER WORDS OF THE SAME CLASS IN A SYNTACTIC DEPENDENCY NETWORK

2007 ◽  
Vol 17 (07) ◽  
pp. 2453-2463 ◽  
Author(s):  
RAMON FERRER I CANCHO ◽  
ANDREA CAPOCCI ◽  
GUIDO CALDARELLI
Keyword(s):  
Community Structure ◽  
Spectral Methods ◽  
Null Hypothesis ◽  
Cluster Size ◽  
Small World ◽  
Power Law Distribution ◽  
Long Range Correlations ◽  
Scale Free ◽  
Dependency Network ◽  
Syntactic Dependency

We analyze here a particular kind of linguistic network where vertices represent words and edges stand for syntactic relationships between words. The statistical properties of these networks have been recently studied and various features such as the small-world phenomenon and a scale-free distribution of degrees have been found. Our work focuses on four classes of words: verbs, nouns, adverbs and adjectives. Here, we use spectral methods sorting vertices. We show that the ordering clusters words of the same class. For nouns and verbs, the cluster size distribution clearly follows a power-law distribution that cannot be explained by a null hypothesis. Long-range correlations are found between vertices in the ordering provided by the spectral method. The findings support the use of spectral methods for detecting community structure.

A syntactic dependency network approach to the study of translational language

2020 ◽  
Author(s):  
Lu Fan ◽  
Yue Jiang
Keyword(s):  
Complex Network ◽  
Native Language ◽  
Small World ◽  
Network Approach ◽  
Global Features ◽  
Scale Free ◽  
Source Language ◽  
Network Parameters ◽  
Dependency Network ◽  
Syntactic Dependency

Abstract Complex network approach provides language research with quantitative measures that can capture global features of language. Although translational language has been recognized as a ‘third code’ by some researchers, its independence still calls for further and quantitative validation in an overall manner. In this study, we intend to examine this independence and explore comprehensively its features. We investigated macroscopically translational language from English into Chinese and from Chinese into English by comparing with its source language and native language through syntactic dependency networks. The results show that: (1) translational language presents small-world and scale-free properties like most languages do; (2) however, it is independent of and different from both source language and native language in terms of its network parameters; (3) its network parameters show values eclectic between source language and native language, and this eclectic tendency may be regarded as a new candidate for universal features of translational language, which certainly needs further validation in other genres and language pairs. This study also corroborates that quantitative linguistic method of complex network approach can be well utilized in the study of translational language.

SZNAJD MODEL AND PROPORTIONAL ELECTIONS: THE ROLE OF THE TOPOLOGY OF THE NETWORK

2009 ◽  
Vol 20 (06) ◽  
pp. 979-990 ◽  
Author(s):  
FABIO STUCCHI VANNUCCHI ◽  
CARMEN P. C. PRADO
Keyword(s):  
Community Structure ◽  
Order Parameter ◽  
High Dimensionality ◽  
Power Law Distribution ◽  
Scale Free ◽  
Absorbing State ◽  
Regular Lattices ◽  
Scale Properties ◽  
Proportional Elections

The Sznajd model (SM) has been employed with success in the last years to describe opinion propagation in a community. In particular, it has been claimed that its transient is able to reproduce some scale properties observed in data of proportional elections, in different countries, if the community structure (the network) is scale-free. In this work, we investigate the properties of the transient of a particular version of the SM, introduced by Bernardes and co-authors in 2002. We studied the behavior of the model in networks of different topologies through the time evolution of an order parameter known as interface density, and concluded that regular lattices with high dimensionality also leads to a power-law distribution of the number of candidates with v votes. Also, we show that the particular absorbing state achieved in the stationary state (or else, the winner candidate), is related to a particular feature of the model, that may not be realistic in all situations.

A Small-World Model of Scale-Free Networks: Features and Verifications

2011 ◽  
Vol 50-51 ◽  
pp. 166-170 ◽  
Author(s):  
Wen Jun Xiao ◽  
Shi Zhong Jiang ◽  
Guan Rong Chen
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Degree Sequence ◽  
Small World ◽  
Static Condition ◽  
Vertex Degree ◽  
Power Law Distribution ◽  
Least Common Multiple ◽  
Scale Free ◽  
Vertex Degrees

It is now well known that many large-sized complex networks obey a scale-free power-law vertex-degree distribution. Here, we show that when the vertex degrees of a large-sized network follow a scale-free power-law distribution with exponent  2, the number of degree-1 vertices, if nonzero, is of order N and the average degree is of order lower than log N, where N is the size of the network. Furthermore, we show that the number of degree-1 vertices is divisible by the least common multiple of , , . . ., , and l is less than log N, where l = < is the vertex-degree sequence of the network. The method we developed here relies only on a static condition, which can be easily verified, and we have verified it by a large number of real complex networks.

scholarly journals An interolog-based barley interactome as an integration framework for immune signaling

2021 ◽  
Author(s):  
Valeria Velásquez-Zapata ◽  
James Mitch Elmore ◽  
Gregory Fuerst ◽  
Roger Wise
Keyword(s):  
Powdery Mildew ◽  
Cellular Response ◽  
Time Course ◽  
Small World ◽  
Power Law Distribution ◽  
Integration Framework ◽  
Scale Free ◽  
Receptor Localization ◽  
Recognition Specificity ◽  
Barley Protein

The barley MLA nucleotide-binding, leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many cereal diseases, including powdery mildew, stem and stripe rust, Victoria blight, and rice blast. We used interolog inference to construct a barley protein interactome (HvInt) comprising 66133 edges and 7181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared to the experimentally validated Arabidopsis interactome of 11253 proteins and 73960 interactions, verifying that the two networks share scale-free properties, including a power-law distribution and small-world network. Then, by successive layering of defense-specific 'omics' datasets, HvInt was customized to model cellular response to powdery mildew infection. Integration of HvInt with expression quantitative trait loci (eQTL) enabled us to infer disease modules and responses associated with fungal penetration and haustorial development. Next, using HvInt and an infection-time-course transcriptome, we assembled resistant (R) and susceptible (S) subnetworks. The resulting differentially co-expressed (R-S) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to Mla through trans eQTL associations. Lastly, next-generation, yeast-two-hybrid screens identified fifteen novel MLA interactors, which were incorporated into HvInt, to predict receptor localization, and signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity.

Cascading failures in complex networks with community structure

2014 ◽  
Vol 25 (05) ◽  
pp. 1440005 ◽  
Author(s):  
Guoqiang Lin ◽  
Zengru Di ◽  
Ying Fan
Keyword(s):  
Community Structure ◽  
Community Networks ◽  
Random Networks ◽  
Cascading Failures ◽  
Power Law Distribution ◽  
Avalanche Size ◽  
Scale Free ◽  
High Betweenness ◽  
Failure Scenario ◽  
The Relationship

Much empirical evidence shows that when attacked with cascading failures, scale-free or even random networks tend to collapse more extensively when the initially deleted node has higher betweenness. Meanwhile, in networks with strong community structure, high-betweenness nodes tend to be bridge nodes that link different communities, and the removal of such nodes will reduce only the connections among communities, leaving the networks fairly stable. Understanding what will affect cascading failures and how to protect or attack networks with strong community structure is therefore of interest. In this paper, we have constructed scale-free Community Networks (SFCN) and Random Community Networks (RCN). We applied these networks, along with the Lancichinett–Fortunato–Radicchi (LFR) benchmark, to the cascading-failure scenario to explore their vulnerability to attack and the relationship between cascading failures and the degree distribution and community structure of a network. The numerical results show that when the networks are of a power-law distribution, a stronger community structure will result in the failure of fewer nodes. In addition, the initial removal of the node with the highest betweenness will not lead to the worst cascading, i.e. the largest avalanche size. The Betweenness Overflow (BOF), an index that we developed, is an effective indicator of this tendency. The RCN, however, display a different result. In addition, the avalanche size of each node can be adopted as an index to evaluate the importance of the node.

scholarly journals An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Yongliang Deng ◽  
Liangliang Song ◽  
Zhipeng Zhou ◽  
Ping Liu
Keyword(s):  
Coal Mine ◽  
Path Length ◽  
Risk Control ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Mine Safety ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
Scale Free

Capturing the interrelations among risks is essential to thoroughly understand and promote coal mining safety. From this standpoint, 105 risks and 135 interrelations among risks had been identified from 126 typical accidents, which were also the foundation of constructing coal mine risk network (CMRN). Based on the complex network theory and Pajek, six parameters (i.e., network diameter, network density, average path length, degree, betweenness, and clustering coefficient) were employed to reveal the topological properties of CMRN. As indicated by the results, CMRN possesses scale-free network property because its cumulative degree distribution obeys power-law distribution. This means that CMRN is robust to random hazard and vulnerable to deliberate attack. CMRN is also a small-world network due to its relatively small average path length as well as high clustering coefficient, implying that accident propagation in CMRN is faster than regular network. Furthermore, the effect of risk control is explored. According to the result, it shows that roof collapse, fire, and gas concentration exceeding limit refer to three most valuable targets for risk control among all the risks. This study will help offer recommendations and proposals for making beforehand strategies that can restrain original risks and reduce accidents.

scholarly journals The inherent community structure of hyperbolic networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bianka Kovács ◽  
Gergely Palla
Keyword(s):  
Community Structure ◽  
Random Graphs ◽  
Small World ◽  
Label Propagation ◽  
Hyperbolic Distance ◽  
Scale Free ◽  
Wide Range ◽  
Community Finding ◽  
Optimisation Model ◽  
Low Dimensional

AbstractA remarkable approach for grasping the relevant statistical features of real networks with the help of random graphs is offered by hyperbolic models, centred around the idea of placing nodes in a low-dimensional hyperbolic space, and connecting node pairs with a probability depending on the hyperbolic distance. It is widely appreciated that these models can generate random graphs that are small-world, highly clustered and scale-free at the same time; thus, reproducing the most fundamental common features of real networks. In the present work, we focus on a less well-known property of the popularity-similarity optimisation model and the $${\mathbb {S}}^1/{\mathbb {H}}^2$$ S 1 / H 2 model from this model family, namely that the networks generated by these approaches also contain communities for a wide range of the parameters, which was certainly not an intention at the design of the models. We extracted the communities from the studied networks using well-established community finding methods such as Louvain, Infomap and label propagation. The observed high modularity values indicate that the community structure can become very pronounced under certain conditions. In addition, the modules found by the different algorithms show good consistency, implying that these are indeed relevant and apparent structural units. Since the appearance of communities is rather common in networks representing real systems as well, this feature of hyperbolic models makes them even more suitable for describing real networks than thought before.

scholarly journals Simulating Market Dynamics: Interactions between Consumer Psychology and Social Networks

2003 ◽  
Vol 9 (4) ◽  
pp. 343-356 ◽  
Author(s):  
Marco A. Janssen ◽  
Wander Jager
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Small World ◽  
Systematic Investigation ◽  
Previous Article ◽  
Market Dynamics ◽  
Power Law Distribution ◽  
Scale Free ◽  
Behavioral Rules ◽  
Node Connectivity

Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).

Social Community Classification Based on Laplacian Spectrum Feature

2011 ◽  
Vol 63-64 ◽  
pp. 142-146
Author(s):  
Bo Wang ◽  
Yi Qiong Xu ◽  
Yao Ming Zhou
Keyword(s):  
Community Structure ◽  
Common Property ◽  
Distance Measure ◽  
Network Models ◽  
Small World ◽  
Laplacian Spectrum ◽  
Free Graph ◽  
Scale Free ◽  
Spectrum Feature ◽  
Complex Network Models

Community structure is a common property that exists in social networks. Community structure analysis is important for understanding network structure and analyzing the network characteristics. Recently community detecting methods are reported continually, but within community the structure is still complex. This paper proposed a method applied to classify community by using Laplacian spectrum feature, and defined the distance measure between the features extracted from difference community. As experiments, this paper studied three complex network models: the random graph of Erdös-Rényi, the small world of Watts and Strogatz and the scale-free graph, and classified them based on Laplacian spectrum feature successfully. The result shows the Laplacian spectrum feature and similarity measure are effective for classification.

scholarly journals On the Distributions of Subgraph Centralities in Complex Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Faxu Li ◽  
Liang Wei ◽  
Haixing Zhao ◽  
Feng Hu
Keyword(s):  
Complex Networks ◽  
Probability Distributions ◽  
Network Connectivity ◽  
Network Models ◽  
Small World ◽  
Small World Networks ◽  
Power Law Distribution ◽  
Scale Free ◽  
Scale Free Networks ◽  
Complex Network Models

Subgraph centrality measure characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than large ones, which makes this measure appropriate for characterizing network motifs. This measure is better in being able to discriminate the nodes of a network than alternate measures. In this paper, the important issue of subgraph centrality distributions is investigated through theory-guided extensive numerical simulations, for three typical complex network models, namely, the ER random-graph networks, WS small-world networks, and BA scale-free networks. It is found that these three very different types of complex networks share some common features, particularly that the subgraph centrality distributions in increasing order are all insensitive to the network connectivity characteristics, and also found that the probability distributions of subgraph centrality of the ER and of the WS models both follow the gamma distribution, and the BA scale-free networks exhibit a power-law distribution with an exponential cutoff.

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