18 research outputs found
Discovering Large Subsets with High Quality Bisections in Real World Graphs
No full textGiven a real world graph, how can we find a large subgraph whose partition quality is much better than the original? Graph partitioning has received great attentions in graph mining, and especially balanced graph partitioning is required in many real world applications. However, the balanced graph partitioning is known to be NP-hard, and moreover it is known that there is no good cut at a large scale for real graphs. Due to this difficulty, in this paper, we propose a new paradigm for graph partitioning. Instead of dealing with the whole graph, our focus is on finding a large subgraph with high quality partitions, in terms of conductance. We show that removing problematic nodes, i.e. large degree nodes called hub nodes in real graphs, remarkably decreases conductance for the remaining giant connected component (GCC), while the number of nodes in the GCC is still significant. In experiments, we demonstrate that our method finds a subgraph of quite a large size with low conductance graph partitions, compared with competing methods. We also show that the competitors cannot find connected subgraphs while our method does, by construction. This improvement in partition quality for the subgraph is especially noticeable for large scale cuts—for a balanced partition, down to 14% of the original conductance with GCC size 70% of the total. As a result, the found subgraph has clear partitions at almost all scales compared with the original, and this result especially helps find communities which are well-formed, but hidden by hubs at various scales in real world graphs like social networks
Hierarchical ordering with partial pairwise hierarchical relationships on the macaque brain data sets.
No full textHierarchical organizations of information processing in the brain networks have been known to exist and widely studied. To find proper hierarchical structures in the macaque brain, the traditional methods need the entire pairwise hierarchical relationships between cortical areas. In this paper, we present a new method that discovers hierarchical structures of macaque brain networks by using partial information of pairwise hierarchical relationships. Our method uses a graph-based manifold learning to exploit inherent relationship, and computes pseudo distances of hierarchical levels for every pair of cortical areas. Then, we compute hierarchy levels of all cortical areas by minimizing the sum of squared hierarchical distance errors with the hierarchical information of few cortical areas. We evaluate our method on the macaque brain data sets whose true hierarchical levels are known as the FV91 model. The experimental results show that hierarchy levels computed by our method are similar to the FV91 model, and its errors are much smaller than the errors of hierarchical clustering approaches
Process design of onboard membrane carbon capture and liquefaction systems for LNG-fueled ships
No full textThis study proposes an onboard membrane carbon capture and liquefaction system for LNG-fueled ships to satisfy the IMO's 2050 greenhouse gas reduction targets. The exhaust gas from a natural gas ship has a low CO2 fraction (similar to 3%) and high O-2 fraction (similar to 16%) compared to the flue gas from power plants. Herein, considering the above distinguishing features, a membrane carbon capture and liquefaction system has been proposed that is energy efficient and compact for the application of ships. To ascertain the performance of the proposed membrane-based system, it is compared to an amine-based onboard system in terms of energy consumption and major equipment size. This work evaluates four process configurations by varying the number of membrane stages and associated liquefaction processes at different CO2/N-2 selectivity and CO2 permeance. The results show that energy consumption (3.98 GJ(e)/t(LCO2)) is higher than the amine-based system (3.07 GJ(e)/t(LCO2)) at the CO2/N-2 selectivity of 50, but it can be decreased to 3.14 and 2.82 (GJ(e)/t(LCO2)) with an improved selectivity of 100 and 150, respectively. The major equipment size decreases to 54%, 28%, and 20% of the amine-based system when the permeance is 1000, 2000, and 3000 GPU, respectively. The results indicate that the new onboard membrane carbon capture and liquefaction system can be a competitive solution for the IMO's greenhouse gas reduction targets for 2050.N
Fast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams
No full textFast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams
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