Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks

Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them without extensive feature engineering. Prior work has shown how to generate network embeddings while preserving the structural role proximity of nodes. These methods, however, cannot capture the temporal evolution of the structural identity of the nodes in dynamic networks. Other works, on the other hand, have focused on learning microscopic dynamic embeddings. Though these methods can learn node representations over dynamic networks, these representations capture the local context of nodes and do not learn the structural roles of nodes. In this article, we propose a novel method for learning structural node embeddings in discrete-time dynamic networks. Our method, called HR2vec , tracks historical topology information in dynamic networks to learn dynamic structural role embeddings. Through experiments on synthetic and real-world temporal datasets, we show that our method outperforms other well-known methods in tasks where structural equivalence and historical information both play important roles. HR2vec can be used to model dynamic user behavior in any networked setting where users can be represented as nodes. Additionally, we propose a novel method (called network fingerprinting) that uses HR2vec embeddings for modeling whole (or partial) time-evolving networks. We showcase our network fingerprinting method on synthetic and real-world networks. Specifically, we demonstrate how our method can be used for detecting foreign-backed information operations on Twitter.

Cyber Attacks Visualization and Prediction in Complex Multi-Stage Network

In network security, various protocols exist, but these cannot be said to be secure. Moreover, is not easy to train the end-users, and this process is time-consuming as well. It can be said this way, that it takes much time for an individual to become a good cybersecurity professional. Many hackers and illegal agents try to take advantage of the vulnerabilities through various incremental penetrations that can compromise the critical systems. The conventional tools available for this purpose are not enough to handle things as desired. Risks are always present, and with dynamically evolving networks, they are very likely to lead to serious incidents. This research work has proposed a model to visualize and predict cyber-attacks in complex, multilayered networks. The calculation will correspond to the cyber software vulnerabilities in the networks within the specific domain. All the available network security conditions and the possible places where an attacker can exploit the system are summarized.

Significance of Short Story Cycle in Rachel Joyce’s A Snow Garden and Other Stories: An Analytical Study Using Complexity Theory

Rachel Joyce’s short story collection A Snow Garden and Other Stories (2015) is composed of seven stories which occur during a fortnight of the holiday, Christmas season. The collection uses narrative techniques which make it a unique set of stories. The stories have an urban setting and examine the intricacies of human relationships. The sense of interconnection highlighted by Joyce in the stories elevates it to a short story cycle. A short story cycle consists of individual stories which can stand on their own as complete narratives while also maintaining fictional links running through all the stories. The paper is an attempt to establish A Snow Garden and Other Stories as a short story cycle. It also argues that by narrating the interconnected nature of human lives Joyce’s work is exploring life as a complex system. As a scientific philosophy complexity theory explores the behavior of complex systems including human societies. Complex systems are self-organizing, dynamic, evolving networks that operate without any centralized control, similar to human societies. This paper will apply the principles of complex systems to reveal patterns of human behavior represented in Joyce’s work.

Analysis of Regular Patterns in Un-weighted Directed Graphs

Time evolving networks tend to have an element of regularity. This regularity is characterized by existence of repetitive patterns in the data sequences of the graph metrics. As per our research, the relevance of such regular patterns to the network has not been adequately explored. Such patterns in certain data sequences are indicative of properties like popularity, activeness etc. which are of vital significance for any network. These properties are closely indicated by data sequences of graph metrics - degree prestige, degree centrality and occurrence. In this paper, (a) an improved mining algorithm has been used to extract regular patterns in these sequences, and (b) a methodology has been proposed to quantitatively analyse the behavior of the obtained patterns. To analyze this behavior, a quantification measure coined as "Sumscore" has been defined to compare the relative significance of such patterns. The patterns are ranked according to their Sumscores and insights are then drawn upon it. The efficacy of this method is demonstrated by experiments on two real world datasets.

The Framework of 6G Self-Evolving Networks and the Decision-Making Scheme for Massive IoT

The increasingly huge amount of device connections will transform the Internet of Things (IoT) into the massive IoT. The use cases of massive IoT consist of the smart city, digital agriculture, smart traffic, etc., in which the service requirements are different and even constantly changing. To fulfill the different requirements, the networks must be able to automatically adjust the network configuration, architectures, resource allocations, and other network parameters according to the different scenarios to match the different service requirements in massive IoT, which are beyond the abilities of the fifth generation (5G) networks. Moreover, the sixth generation (6G) networks are expected to have endogenous intelligence, which can well support the massive IoT application scenarios. In this paper, we first propose the framework of the 6G self-evolving networks, in which the autonomous decision-making is one of the vital parts. Then, we introduce the autonomous decision-making methods and analyze the characteristics of the different methods and mechanisms for 6G networks. To prove the effectiveness of the proposed framework, we consider one of the typical scenarios of massive IoT and propose an artificial intelligence (AI)-based distributed decision-making algorithm to solve the problem of the offloading policy and the network resource allocation. Simulation results show that the proposed decision-making algorithm with the self-evolving networks can improve the quality of experience (QoE) compared with the lower training.

DynComm

The analysis of dynamics in networks represents a great deal in the Social Network Analysis research area. To support students, teachers, developers, and researchers in this work, we introduce a novel R package, namely DynComm. It is designed to be a multi-language package used for community detection and analysis on dynamic networks. The package introduces interfaces to facilitate further developments and the addition of new and future developed algorithms to deal with community detection in evolving networks. This new package aims to abstract the programmatic interface of the algorithms, whether they are written in R or other languages, and expose them as functions in R.

GAEN: Graph Attention Evolving Networks

Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.