Bayesian Analysis of ERG Models for Multilevel, Multiplex, and Multilayered Networks with Sampled or Missing Data

Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

Advances in exponential random graph ( p*) models applied to a large social network

RA-HGNN: Attribute completion of heterogeneous graph neural networks based on residual attention mechanism

Link prediction based on bipartite networks can not only mine hidden relationships between different types of nodes, but also reveal the inherent law of network evolution. Existing bipartite network link prediction is mainly based on the global structure that cannot analyze the role of the local structure in link prediction. To tackle this problem, this paper proposes a deep link-prediction (DLP) method by leveraging the local structure of bipartite networks. The method first extracts the local structure between target nodes and observes structural information between nodes from a local perspective. Then, representation learning of the local structure is performed on the basis of the graph neural network to extract latent features between target nodes. Lastly, a deep-link prediction model is trained on the basis of latent features between target nodes to achieve link prediction. Experimental results on five datasets showed that DLP achieved significant improvement over existing state-of-the-art link prediction methods. In addition, this paper analyzes the relationship between local structure and link prediction, confirming the effectiveness of a local structure in link prediction.

Robustness of interdependent supply chain networks against both functional and structural cascading failures

The high-level decision-making is time-consuming in a large, complex circumstance. The decision-maker needs to consider many factors whatever they are in same domain or not. Influence diagram (ID) is an effective tool to help people make a strategic decision. In a large influence diagram, the high-level decision-maker doesn't need to know the details, and he only needs to know the overall effects of the decisions made in each domain. Starting from this real application, it is necessary to aggregate the nodes in a domain into one block in an influence diagram. In this paper, we are to discuss the aggregation of nodes in influence diagrams. In order to aggregate nodes, we have to partition the influence diagram into blocks first. Each block is composed of nodes which contact each other strongly and contact other nodes loosely. Since the influence diagram has different types of nodes, we have to partition these types of nodes respectively. Based on the relationships among nodes, we aggregate each block into a new node. Further, we combine the blocks with different types of nodes and get a new influence diagram. Preliminary experiments show the feasibility of our proposed methods.

Evolving heterogeneous networks, which contain different types of nodes and links that change over time, appear in many domains including protein---protein interactions, scientific collaborations, telecommunications. In this paper, we aim to discover temporal information from a heterogenous evolving network in order to improve node classification. We propose a framework, Genetic Algorithm enhanced Time Varying Relational Classifier for evolving Heterogeneous Networks (GA-TVRC-Het), to extract the effects of different relationship types in different time periods in the past. These effects are discovered adaptively by utilizing genetic algorithms. A relational classifier is extended as the classification method in order to be able to work with different types of nodes. The proposed framework is tested on two real world data sets. It is shown that using the optimal time effect improves the classification performance to a large extent. It is observed that the optimal time effect does not necessarily follow a certain functional trend, for example linear or exponential decay in time. Another observation is that the optimal time effect may be different for each type of interaction. Both observations reveal the reason why GA-TVRC-Het outperforms methods that rely on a predefined form of time effect or the same time effect for each link type.

The mobility factor of the nodes in a mobile Ad Hoc networks (MANET) changes the network topology leading to changes in size of the network. As the topology changes, link failure between the nodes takes place due to several reasons like channel interference and dynamic obstacles that give rise to severe performance degradation. Scalability and link failure recovery are the main criteria that will determine the performance of the network in terms of quality of service (QoS). While having different type of nodes, both mobile and immobile, that have several performance severity related to data transfer which is similar to the one in industrial parameter reporting and data logging, there exists a need to overcome performance related issues such as relatively faster and secured data transfer within the set of nodes. We developed the Quantum based Routing protocol (QRP) associated with Local Link Failure Recovery Algorithm (LLFR). The QRP is a routing protocol that uses DSR and AODV as an underlying protocol to improve the QoS in scalable wireless network. The QRP and LLFR establish link failure recovery spontaneously at the point of link breakage. The performance parameters like; packet delivery ratio, throughput, average end to end delay and routing overhead of the routing protocol QRP with LLFR is analyzed using NS2 simulator. Keywords: Ad Hoc Network, Link Failure, Routing Protocols, Scalability, Quality of Service.

Considering the characteristic of heterogeneous wireless sensor networks, the clustering algorithm based on data content for heterogeneous sensor network is proposed. In cluster construction stage, according to the various sensor node types in CADC algorithm, the same types of nodes is divided into the same cluster and different types of nodes can not be divided into the same cluster. In the cluster heads selection scheme, nodes with the most residual energy will be elected cluster heads first. Cluster heads with the same type further integrate into the secondary cluster. Because this algorithm makes the data aggregation to be the most optimal and reduces the transmission distance of cluster heads, it can prolong the lifetime of the whole networks.

Railway network delay evolution: A heterogeneous graph neural network approach

Mapping network flows provides insight into the organization of networks, but even though many real networks are bipartite, no method for mapping flows takes advantage of the bipartite structure. What do we miss by discarding this information and how can we use it to understand the structure of bipartite networks better? The map equation models network flows with a random walk and exploits the information-theoretic duality between compression and finding regularities to detect communities in networks. However, it does not use the fact that random walks in bipartite networks alternate between node types, information worth 1 bit. To make some or all of this information available to the map equation, we developed a coding scheme that remembers node types at different rates. We explored the community landscape of bipartite real-world networks from no node-type information to full node-type information and found that using node types at a higher rate generally leads to deeper community hierarchies and a higher resolution. The corresponding compression of network flows exceeds the amount of extra information provided. Consequently, taking advantage of the bipartite structure increases the resolution and reveals more network regularities.

A relation-aware heterogeneous graph convolutional network for relationship prediction

Since the 1970s, communication researchers have utilized social network analysis to understand mass, health, organizational, and interpersonal communication. This article introduces communication researchers to a new class of social network analysis methods, exponential random graph (p*) models. This new method represents the latest advancement in social network methodology and will enhance the trajectory of social network research in the communication discipline. The benefits of this class of models include allowing for the simultaneous estimation of attribute and structural parameters, accounting for the interdependent nature of network data, and retaining the complexity of network observations throughout the analysis. An example analysis using data from Shumate, Fulk, and Monge (2005) is provided to illustrate the potentials of exponential random graph modeling. Five different social network software programs capable of the analysis discussed in this article are introduced with regard to their respective benefits. Finally, a brief tutorial based on data from Palazzolo (2005) is given on how to conduct an ERGM analysis using the PNET software program.

Synthetic power grids enable real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel method for the generation of synthetic transmission power grids using exponential random graph (ERG) models. Our two main contributions are (1) the formulation of an ERG model tailored specifically for capturing the topological nuances of power grids and (2) a general procedure for estimating the parameters of such a model conditioned on working with connected graphs. From a modeling perspective, we identify edge counts per bus type and k-triangles as crucial topological characteristics for synthetic power-grid generation. From a technical perspective, we develop a rigorous methodology to estimate the parameters of an ERG constrained to the space of connected graphs. The proposed model is flexible, easy to implement, and successfully captures the desired topological properties of power grids. Published by the American Physical Society 2024

Despite the importance of social network analysis (SNA) in public administration, comprehensive reviews on this topic are rare. This study aims to fill this gap by focusing on the application of SNA in network research within public administration. This research analyzes 144 empirical papers from top public administration journals (January 1997–January 2022), utilizing bibliometric and content analyses. The study identifies social capital, the Advocacy Coalition Framework (ACF), the ecology of games framework, and resource dependence theory as the predominant theoretical foundations of SNA. The primary focus of these works is on collective action problems and public service delivery, with a trend towards the integration and comparison of theories. Through content analysis, we find a notable shift in SNA application from descriptive analysis to network inference, predominantly using Exponential Random Graph Models (ERGMs). It also reveals the growing significance of network visualization in SNA. The study concludes with potential future directions for SNA in public administration, highlighting its evolving role and significance.