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© 1989-2012 IEEE. Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the graph construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for graph construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate graph initialized with link data; in graph distillation step, the proposed method updates and refines the graph based on only the selected features. The final resulting graph, which is referred to as the distilled graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.

© 1989-2012 IEEE. Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the graph construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for graph construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate graph initialized with link data; in graph distillation step, the proposed method updates and refines the graph based on only the selected features. The final resulting graph, which is referred to as the distilled graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.

The main constrain for effective gas drainage in coal mines is the low permeability nature of coal reservoirs. As coal mining activities are extending to deeper subsurface, the ever-increasing in situ stress conditions is anticipated to result in much lower permeability and more challenges for gas emission control in coal mines. In recent years, hydraulic slotting using high-pressure waterjet along underground gas drainage boreholes, as a general solution to stimulate low permeability coal seams, has become increasingly favourable. This paper presents a systematic investigation into the sensitivity of borehole slotting performance to a number of field and operational parameters. A wide range of geomechanical properties, in situ stress conditions, slot geometry and spacing of multiple slots were considered in a series of numerical simulations. The relations between these key parameters and the failure zone size/volume induced by slotting were quantified. The effect of different parameters in improving slotting performance has also been ranked, which provides theoretical base for mine operators to optimise slotting operations.

Coal and gas outbursts have long posed a serious risk to safe and efficient production in coal mines. It is recognised that the coal and gas outbursts are triggered by excavation unloading followed by gas-driven rapid propagation of a system of preexisting or mining-induced fractures. Gas-filled fractures parallel to a working face are likely to experience opening first, then expansion and rapid propagation stages under unloading conditions. This research aimed to identify the key factors affecting outburst initiation and its temporal evolution during roadway developments. Specifically, the response of pre-set fractures in a coal seam sandwiched between rock layers to roadway development is simulated using a geomechanical model coupled with fracture mechanics for fracture opening and propagation. In addition, kinetic gas desorption and its migration into open fractures is considered. During simulations outburst is deemed to occur when the fracture length exceeds the dimension of a host element. The findings of this research suggests that the simulated coal and gas outburst may be considered as a dynamic gas desorption-driven fracture propagation process. The occurrence of coal and gas outbursts is found to be influenced mainly by the coal properties, fracture attributes, and initial gas pressure and the in situ stress conditions.

Microseismicity has long been a precursor for underground mining hazards such as rockbursts and coal and gas outbursts. In this research, a methodology combining deterministic stress and failure analysis and stochastic fracture slip evaluation, based upon the widely accepted fracture slip seismicity-generation mechanism, has been developed to simulate microseismic events induced by longwall mining. Using the built-in DFN facility in FLAC3D, discrete fractures following a power law size distribution are distributed throughout a 3D continuum model in a probabilistic way to account for the stochastic nature of microseismicity. The DFN-based modelling approach developed was adopted to simulate the evolution of microseismicity induced by the progressive face advance in a longwall top coal caving (LTCC) panel at Coal Mine Velenje, Slovenia. At each excavation step, global stress and failure analysis with reference to the strain-softening post-failure behaviour characteristic of coal, and fracture slip evaluation for microseismicity are conducted sequentially. The model findings are compared to the microseismic event data recorded during a long-term field monitoring campaign conducted at the same LTCC panel. It was found that the released energy and frequency-magnitude distribution of microseismicity are associated with the slipped fracture sizes and fracture size distribution. These features for recorded microseismic events were fairly constant until a xylite rich heterogeneous zone ahead of the working face was approached, which indicates that fractures within the extracted coal seam follow the same size distribution. The features obtained from modelled microseismic events were consistent over the production period, and matched well the field observations. Furthermore, the model results indicate that the power law fracture size distribution can be used to model longwall-mining-induced microseismicity. This model provides a unique prospective to understand longwall coal m