Scale-free homophilic network

Journal of Complex Networks

2017

In this paper we consider the dyadic effect introduced in complex networks when nodes are distinguished by a binary characteristic. Under these circumstances two independent parameters, namely dyadicity and heterophilicity, are able to measure how much the assigned characteristic affects the network topology. All possible configurations can be represented in a phase diagram lying in a two-dimensional space that represents the feasible region of the dyadic effect, which is bound by two upper bounds on dyadicity and heterophilicity. Using some network's structural arguments, we are able to improve such upper bounds and introduce two new lower bounds, providing a reduction of the feasible region of the dyadic effect as well as constraining dyadicity and heterophilicity within a specific range. Some computational experiences show the bounds' effectiveness and their usefulness with regards to different classes of networks. Keywords: Complex networks, dyadic effect, upper and lower bound. arXiv:1609.04547v1 [math.CO] 15 Sep 2016 Nodes' characteristics and dyadic effectHerein, we refer to a given characteristic c i , which can assume the values 0 or 1, for each i ∈ N . Consequently, N can be divided into two subsets: the set of n 1 nodes with characteristic c i = 1, the set of n 0 nodes with characteristic c i = 0; thus, N = n 1 + n 0 .We distinguish three kinds of dyads, i.e. edges and their two end nodes, in the network:(1 − 1), (1 − 0), and (0 − 0) as depicted in the Figure 1.We label the number of each dyad in the graph as m 11 , m 10 , m 00 , respectively. Hence, M = m 11 +m 10 +m 00 . We consider m 11 and m 10 as independent parameters that represent i∈D T G (n 1 ) d i − n 1 (n 1 − 1). Indeed, every stub that constitutes the residual degree will find its endpoint in a node with c i = 0. Otherwise, i∈D T G (n 1 ) d i < n 1 (n 1 − 1) and m 10 = 1.

Section: Problem Settings 21 Theoretical Backgroundmentioning

confidence: 99%

Structural bounds on the dyadic effect

Cinelli

Journal of Complex Networks

2017

“…We consider m 11 and m 10 as independent parameters that represent the dyads containing nodes with property 1. If a casual setting among the N nodes is considered, where any node has an equal chance of having the property 1, the values of m 11 and m 10 are (de Almeida et al, 2013):…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The relevant deviations of m 11 and m 10 from the expected values m 11 and m 10 denote that the property 1 is not randomly distributed (de Almeida et al, 2013;Park and Barabási, 2007).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…1 Although research is focused mainly on the network's structure, in many systems the nodes themselves have peculiar characteristics, which carry significant information regarding their role in the topology of the network. Indeed, in several systems, adjacent nodes show relevant correlations in their features meaning that they have a higher chance to connect to each other (de Almeida et al, 2013;Škerlavaj et al, 2010). Thus, the number of links between nodes is larger than expected if the characteristics are randomly distributed due to the dyadic effect (Park and Barabási, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the influence of nodes' characteristic in inter-organisational innovation networks structure

Pratesi

2016

IJCEE

This paper studies inter-organisational innovation networks, investigating the relations between the structure of the connections among members and a given node's characteristic. Such relations are used to provide some insights into the attitude of network innovation. We propose an original methodology that considers nodes belonging to two different classes detecting the available configurations. The intensity of connections, as an additional layer of information, is also included to examine its relative influence on the network topology. We complete the analysis through the introduction of a qualitative intensity/connectance matrix that attempts to connote the innovation attitude of the network. We test the methodology on a real case study, discussing the detected configuration and the implication in terms of innovation attitude.

“…In terms of the BA model, it's possible to say that homophily approaches a topological network in terms of preferential attachment and node similitude [31]. Indeed, homophily tends to create tighter links between members, enhancing network cohesiveness.…”

Section: Homophilymentioning

confidence: 99%

Organizing Collaboration in Inter-Organizational Innovation Networks, from Orchestration to Choreography

International Journal of Engineering Business Management

2015

Innovation networks constitute a valid structure within which to foster members' abilities to interact and cooperate, in order to reduce environmental uncertainty and face the challenges that derive from economic change. Network members take advantage of their participation in the system by creating and extracting value when performing certain deliberate and purposeful activities. Traditionally, the interactions between network members are represented in a hierarchical structure with a prominent member performing a leadership role in managing the system. Recently, more complex organizations have emerged in which the power of the decisions is spread among all partners. In this paper, we overcome the single hub model and propose a new organizational framework called "choreography", which takes into account all network members, extending the roles of coordination and management throughout the entire network.We consider a real inter-organizational innovation network as an eligible case study which supports the choreography model.