With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.Definition 2 (First-order Proximity). The first-order proximity is the local pairwise proximity between two connected vertices [1]. For each vertex pair (v i , v j ), if (v i , v j ) ∈ E, the first-order proximity between v i and v j is w ij ; otherwise, the first-order proximity between v i and v j is 0. The first-order proximity captures the direct neighbor relationships between vertices.Definition 3 (Second-order Proximity and High-order Proximity). The second-order proximity captures the 2-step relations between each pair of vertices [1]. For each vertex pair (v i , v j ), the second order proximity is determined by the number of common neighbors shared by the two vertices, which can also be measured by the 2-step transition probability from v i to v j equivalently. Compared with the second-order proximity, the highorder proximity [26] captures more global structure, which explores k-step (k ≥ 3) relations between each pair of vertices. For each vertex pair (v i , v j ), the higherorder proximity is measured by the k-step (k ≥ 3) transition probability from vertex v i to vertex v j , which can also be reflected by the number of k-step (k ≥ 3) paths from v i to v j . The second-order and high-order proximity capture the similarity between a pair of, indirectly connected, vertices with similar structural contex...

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We describe a new architecture for Byzantine fault tolerant state machine replication that separates agreement that orders requests from execution that processes requests. This separation yields two fundamental and practically significant advantages over previous architectures. First, it reduces replication costs because the new architecture can tolerate faults in up to half of the state machine replicas that execute requests. Previous systems can tolerate faults in at most a third of the combined agreement/state machine replicas. Second, separating agreement from execution allows a general privacy firewall architecture to protect confidentiality through replication. In contrast, replication in previous systems hurts confidentiality because exploiting the weakest replica can be sufficient to compromise the system. We have constructed a prototype and evaluated it running both microbenchmarks and an NFS server. Overall, we find that the architecture adds modest latencies to unreplicated systems and that its performance is competitive with existing Byzantine fault tolerant systems.

This paper describes ongoing work with the Australian Government to detect, assess, summarise, and report messages of interest for crisis coordination published by Twitter. The developed platform and client tools, collectively termed the Emergency Situation Awareness -Automated Web Text Mining (ESA-AWTM) system, demonstrate how relevant Twitter messages can be identified and utilised to inform the situation awareness of an emergency incident as it unfolds.A description of the ESA-AWTM platform is presented detailing how it may be used for real life emergency management scenarios. These scenarios are focused on general use cases to provide: evidence of pre-incident activity; near-realtime notification of an incident occurring; first-hand reports of incident impacts; and gauging the community response to an emergency warning. Our tools have recently been deployed in a trial for use by crisis coordinators.

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