Relating Emerging Adaptive Network Behavior to Network Structure: A Declarative Network Analysis Perspective

In this paper, the challenge for dynamic network modeling is addressed how emerging behavior of an adaptive network can be related to characteristics of the adaptive network’s structure. By applying network reification, the adaptation structure is modeled in a declarative manner as a subnetwork of a reified network extending the base network. This construction can be used to model and analyze any adaptive network in a neat and declarative manner, where the adaptation principles are described by declarative mathematical relations and functions in reified temporal-causal network format. In different examples, it is shown how certain adaptation principles known from the literature can be formulated easily in such a declarative reified temporal-causal network format. The main focus of this paper on how emerging adaptive network behavior relates to network structure is addressed, among others, by means of a number of theorems of the format “properties of reified network structure characteristics imply emerging adaptive behavior properties”. In such theorems, classes of networks are considered that satisfy certain network structure properties concerning connectivity and aggregation characteristics. Results include, for example, that under some conditions on the network structure characteristics, all states eventually get the same value. Similar analysis methods are applied to reification states, in particular for adaptation principles for Hebbian learning and for bonding by homophily, respectively. Here results include how certain properties of the aggregation characteristics of the network structure of the reified network for Hebbian learning entail behavioral properties relating to the maximal final values of the adaptive connection weights. Similarly, results are discussed on how properties of the aggregation characteristics of the reified network structure for bonding by homophily entail behavioral properties relating to clustering and community formation in a social network.

Data-Driven Dynamic Network Modeling for Analyzing the Evolution of Product Competitions

Understanding the impact of engineering design on product competitions is imperative for product designers to better address customer needs and develop more competitive products. In this paper, we propose a dynamic network-based approach for modeling and analyzing the evolution of product competitions using multi-year buyer survey data. The product co-consideration network, formed based on the likelihood of two products being co-considered from survey data, is treated as a proxy of products’ competition relations in a market. The separate temporal exponential random graph model (STERGM) is employed as the dynamic network modeling technique to model the evolution of network as two separate processes: link formation and link dissolution. We use China’s automotive market as a case study to illustrate the implementation of the proposed approach and the benefits of dynamic network models compared to the static network modeling approach based on an exponential random graph model (ERGM). The results show that since STERGM takes preexisting competition relations into account, it provides a pathway to gain insights into why a product may maintain or lose its competitiveness over time. These driving factors include both product attributes (e.g., fuel consumption) as well as current market structures (e.g., the centralization effect). With the proposed dynamic network-based approach, the insights gained from this paper can help designers better interpret the temporal changes of product competition relations to support product design decisions.

An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

As the transmission of public opinion on the Internet in the “We the Media” era tends to be supraterritorial, concealed and complex, the traditional “point-to-surface” transmission of information has been transformed into “point-to-point” reciprocal transmission. A foundation for studies of the evolution of public opinion and its transmission on the Internet in the “We the Media” era can be laid by converting the massive amounts of fragmented information on public opinion that exists on “We the Media” platforms into structurally complex networks of information. This paper describes studies of structurally complex network-based modeling of public opinion on the Internet in the “We the Media” era from the perspective of the development and evolution of complex networks. The progress that has been made in research projects relevant to the structural modeling of public opinion on the Internet is comprehensively summarized. The review considers aspects such as regular grid-based modeling of the rules that describe the propagation of public opinion on the Internet in the “We the Media” era, social network modeling, dynamic network modeling, and supernetwork modeling. Moreover, an outlook for future studies that address complex network-based modeling of public opinion on the Internet is put forward as a summary from the perspective of modeling conducted using the techniques mentioned above.