Abstract
Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities.

Abstract
Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. While most of the existing approaches have focused on discovering communities of nodes, recent studies have shown the advantages and uses of link community discovery in networks. Generative models provide a promising class of techniques for the identification of modular structures in networks, but most generative models mainly focus on the detection of node communities rather than link communities. In this work, we propose a generative model, which is based on the importance of each node when forming links in each community, to describe the structure of link communities. We proceed to fit the model parameters by taking it as an optimization problem, and solve it using nonnegative matrix factorization. Thereafter, in order to automatically determine the number of communities, we extend the above method by introducing a strategy of iterative bipartition. This extended method not only finds the number of communities all by itself, but also obtains high efficiency, and thus it is more suitable to deal with large and unexplored real networks. We test this approach on both synthetic benchmarks and real-world networks including an application on a large biological network, and compare it with two highly related methods. Results demonstrate the superior performance of our approach over competing methods for the detection of link communities.

Abstract
Keeping replicated data strongly consistent is convenient when communication is fast and available. In internet-scale distributed systems the reality of high communication latencies and likelihood of partitions, leads developers to adopt more relaxed consistency models, such as eventual consistency. Conflict-free Replicated Data Types, bring structure to the design of eventually consistent data management solutions, by precisely describing the behaviour under concurrent updates and guarantying a path to reconciliation. This paper offers a survey of the mathematical structures that support state based multi-master replication with reconciliation, and shows how state structures and state transformations can be composed to provide data types that are now used in practice in many geo-replicated systems.

Abstract
Conflict-free Replicated Data Types (CRDTs) are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs ensure convergence through disseminating the entire state, that may be large, and merging it to other replicas. We introduce Delta State Conflict-Free Replicated Data Types (delta-CRDT) that can achieve the best of both operation-based and state-based CRDTs: small messages with an incremental nature, as in operation-based CRDTs, disseminated over unreliable communication channels, as in traditional state-based CRDTs. This is achieved by defining delta-mutators to return a delta-state, typically with a much smaller size than the full state, that to be joined with both local and remote states. We introduce the delta-CRDT framework, and we explain it through establishing a correspondence to current state-based CRDTs. In addition, we present an anti-entropy algorithm for eventual convergence, and another one that ensures causal consistency. Finally, we introduce several delta-CRDT specifications of both well-known replicated datatypes and novel datatypes, including a generic map composition.

Abstract
Concurrent linearizable access to shared objects can be prohibitively expensive in a high contention workload. Many applications apply ad-hoc techniques to eliminate the need for synchronous atomic updates, which may result in non-linearizable implementations. We propose a new model which leverages such patterns for concurrent access to objects in a shared memory system. In this model, each thread maintains different views on the shared object: a thread-local view and a global view. As the thread-local view is not shared, it can be updated without incurring synchronization costs. These local updates become visible to other threads only after the thread-local view is merged with the global view. This enables better performance at the expense of linearizability. We discuss the design of several datatypes and evaluate their performance and scalability compared to linearizable implementations. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Counters are an important abstraction in distributed computing, and play a central role in large scale geo-replicated systems, counting events such as web page impressions or social network likes. Classic distributed counters, strongly consistent via linearisability or sequential consistency, cannot be made both available and partition-tolerant, due to the CAP Theorem, being unsuitable to large scale scenarios. This paper defines Eventually Consistent Distributed Counters (ECDCs) and presents an implementation of the concept, Handoff Counters, that is scalable and works over unreliable networks. By giving up the total operation ordering in classic distributed counters, ECDC implementations can be made AP in the CAP design space, while retaining the essence of counting. Handoff Counters are the first Conflict-free Replicated Data Type (CRDT) based mechanism that overcomes the identity explosion problem in naive CRDTs, such as G-Counters (where state size is linear in the number of independent actors that ever incremented the counter), by managing identities towards avoiding global propagation and garbage collecting temporary entries. The approach used in Handoff Counters is not restricted to counters, being more generally applicable to other data types with associative and commutative operations.