Joint community and anomaly tracking in dynamic networks

arXiv.org Machine Learning

Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection finds many practical applications in diverse settings. Communities in online social networks are indicative of shared functional roles, or affiliation to a common socio-economic status, the knowledge of which is vital for targeted advertisement. In buyer-seller networks, community detection facilitates better product recommendations. Unfortunately, reliability of community assignments is hindered by anomalous user behavior often observed as unfair self-promotion, or "fake" highly-connected accounts created to promote fraud. The present paper advocates a novel approach for jointly tracking communities while detecting such anomalous nodes in time-varying networks. By postulating edge creation as the result of mutual community participation by node pairs, a dynamic factor model with anomalous memberships captured through a sparse outlier matrix is put forth. Efficient tracking algorithms suitable for both online and decentralized operation are developed. Experiments conducted on both synthetic and real network time series successfully unveil underlying communities and anomalous nodes.


Sentiment Analysis on Social Network Data (Twitter, Facebook, etc.)

#artificialintelligence

Sentiment analysis is a useful service for just about any business. It is always valuable to know whether your customers are saying positive or negative things about you. This gives you more flexibility to start with their sample and then tweak it to your needs. Then you would deploy it yourself and call it yourself.


Don't Listen To The 5G Naysayers

Forbes - Tech

Every decade or so, a new generation of telecom network technology comes along that promises more speed, more capacity, better quality and new uses for customers. With each generation, network operators invest capital to upgrade their infrastructure, with the firm belief that doing so will lead to happier customers and reinvigorated revenues and profits. This formulation has been true ever since the early days of cell phone service in the 1980s; it has held up through 2G in the 1990s, 3G in the 2000s and 4G in the 2010s. But this time around, something has changed. When it comes to the next generation, 5G, some telecom executives seem to have lost their faith in the power of technology.


Role-Dynamics: Fast Mining of Large Dynamic Networks

arXiv.org Artificial Intelligence

To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Our novel approach learns the appropriate structural role dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and non-stationary roles, spikes/steps in role-memberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are non-stationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.


5 Multi-Access Edge Computing Use Cases Across Different Industries Lanner

#artificialintelligence

Much of the excitement about multi-access edge computing (MEC) centers around its potential use cases and how it might be combined with other emerging technologies such as 5G and artificial intelligence (AI). However, there are MEC use cases that are being implemented by businesses and organizations right now that can showcase the power of multi-access edge computing for a wide variety of different industries and applications. From customer services and commercial operations to critical infrastructure and the industrial Internet of Things (IoT), multi-access edge computing is allowing network operators to open up their own networks to a completely new IT environment. As we continue to rely on increasingly connected and intelligent systems to help run our day to day business and personal lives, the Internet of Things and MEC technologies could provide the high-bandwidth, low-latency, and real-time access needed by modern systems. In this article, we'll being taking a look at five multi-access edge computing use cases and the benefits they bring to their particular applications.