We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Decentralized autonomy is a long-standing research topic in information sciences and social sciences. The self-organization phenomenon in natural ecosystems, the Cyber Movement Organizations (CMOs) on the Internet, and the Distributed Artificial Intelligence (DAI), and so on, can all be regarded as its early manifestations. In recent years, the rapid development of blockchain technology has spawned the emergence of the so-called Decentralized Autonomous Organization [DAO, sometimes labeled as Decentralized Autonomous Corporation (DAC)], which is a new organization form that the management and operational rules are typically encoded on blockchain in the form of smart contracts, and can autonomously operate without centralized control or third-party intervention. DAO is expected to overturn the traditional hierarchical management model and significantly reduce organizations’ costs on communication, management, and collaboration. However, DAO still faces many challenges, such as security and privacy issue, unclear legal status, and so on. In this article, we strive to present a systematic introduction of DAO, including its concept and characteristics, research framework, typical implementations, challenges, and future trends. Especially, a novel reference model for DAO which employs a five-layer architecture is proposed. This article is aimed at providing helpful guidance and reference for future research efforts.

Decentralized Autonomous Organizations: Concept, Model, and Applications

Proof of work and proof of stake (PoS) are commonly used in the current permissionless blockchains. These consensus protocols can be abstracted into a random process of selecting a node for accounting in a blockchain ledger. However, they are generally faced with resource consumption and vulnerability issues. We present proof of credit (PoC), a fair blockchain protocol based on the PoC blockchain protocol. It is a special PoS protocol where the credit is a special kind of stake quantifying whether the node’s activity is beneficial to the system. Any nodes cannot change their credits arbitrarily. We demonstrate that our PoC protocol satisfies the security properties, including common prefix, chain quality, and chain growth, under the assumption that the total credit the honest held is majority. In addition, we propose a self-audit mechanism and a hybrid incentive mechanism to enhance the security and stability. Finally, we explain the method by which the PoC protocol resists the double-spending attacks and the selfish mining attacks.

A Fair Blockchain Based on Proof of Credit

Technology has continuously been a crucially influenced and acutely tangled with the progress of society. Online Social Networks (OSN) are interesting and valuable datasets that can be leveraged to improve understanding about society and to know inter-personal choices. Identification and Ranking of Influential Nodes (IRIN) is non-trivial task for real time OSN like Twitter which accustom with ever-changing network, demographics and contents having heterogeneous features such as Tweets, Likes, Mentions and Retweets. Existing techniques such as Centrality Measures and Influence Maximization ignores vital information available on OSN, which are inappropriate for IRIN. Most of these approaches have high computational complexity i.e. $$O(n^{3} )$$
 . This research aims to put forward holistic approach using Heterogeneous Surface Learning Features (HSLF) for IRIN on specific topic and proposes two approaches: Average Consensus Ranking Aggregation and Weighted Average Consensus Ranking Aggregation using HSLF. The effectiveness and efficiency of the proposed approaches are tested and analysed using real world data fetched from Twitter for two topics, Politics and Economy and achieved superior results compared to existing approaches. The empirical analysis validate that the proposed approach is highly scalable with low computational complexity and applicable for large datasets.

Consensus-based aggregation for identification and ranking of top- k influential nodes

Social Network Influencer Rank Recommender Using Diverse Features from Topical Graph

In complex network analysis, the problem of ranking individual nodes based on their importance has attracted increasing attention from the scientific community due to its vast application, such as identification of influential spreaders for viral marketing or epidemic control, bottlenecks for traffic congestion control, and so on. The growing literature proposes a number of measures to determine the rank order of the network entities where complete information about the nodes and their interaction is available. Degree centrality, PageRank, eigenvector centrality, closeness centrality are few such popular measures. In most real-life scenarios, however, the information about the underlying network is incomplete or affected due to noise. The few works that study the effects of incomplete information on the rank orders show the vulnerability of the rank orders in various topologies. In this paper, we investigate the effects of noise, both random and nonrandom, on the aggregated rank orders determined from the degree, PageRank, eigenvector centrality, and closeness centrality-based rankings. This paper reveals an important insight that even the simple Borda Count ranking has the potential to improve on the accuracy of rank orders in networks with uncertainty. This paper shows the existence of stable nodes in various networks and indicates that the design of the consensus approach based on the properties of the stable nodes can further improve the stability of the rank orders.

Oishik Chatterjee

Papers

Stability of Consensus Node Orderings Under Imperfect Network Data