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Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet—a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

The National Broadband Network (NBN) is the largest public infrastructure project in the history of Australia. The goal of the NBN is to provide Australians with broadband internet access by using a mix of technologies, ranging from fibre and hybrid fibre-coaxial to fixed wireless and satellite platforms. Although the NBN is a public project, one of the more vexing aspects of its evolution is its lack of data transparency. There is virtually no information on platform use or footprint distribution throughout the country. Not only does this drastically limit evidence-based telecommunications policy analysis, it also limits the ability to evaluate equity in the spatial distribution of broadband connectivity and infrastructure quality. The purpose of this paper is to uncover the geographic presence of the various NBN technologies using data mining techniques and census-based socio-spatial data (SEIFA). The results portray the complexity of the NBN footprint across three metropolitan regions of Sydney, Melbourne, and Brisbane – with a focus on how the mixed-technology platform adopted divides major Australian cities. • The National Broadband Network (NBN) is the largest public infrastructure project in the history of Australia. • There is limited information on the geographic distribution of the various NBN technologies throughout the country. • This paper aims to uncover the NBN footprint using data mining techniques and census-based socio-spatial data (SEIFA). • The results portray the complexity of the NBN footprint across three metropolitan regions of Sydney, Melbourne, and Brisbane. [ABSTRACT FROM AUTHOR]