DSpace Community:http://hdl.handle.net/10203/234662020-07-18T05:18:53Z2020-07-18T05:18:53ZA new biore finery platform for producing (C2-5) bioalcohols through the biological/chemical hybridization processJung, SungyupKim, HanaTsang, Yiu FaiLin, Kun-Yi AndrewPark, Young-KwonKwon, Eilhann E.http://hdl.handle.net/10203/2750782020-07-01T08:20:07Z2020-09-01T00:00:00ZTitle: A new biore finery platform for producing (C2-5) bioalcohols through the biological/chemical hybridization process
Authors: Jung, Sungyup; Kim, Hana; Tsang, Yiu Fai; Lin, Kun-Yi Andrew; Park, Young-Kwon; Kwon, Eilhann E.
Abstract: This review presents an emerging biorefinery platform for C2-5 bioalcohol production through chemical synthesis using the organic waste materials. Bioalcohols are the most commercialized carbon-neutral transportation fuels, compatible with existing an internal combustion (IC) engine. However, current bioalcohol fermentation processes have made from sugar-rich edible crops. Also, carbon loss from the fermentation process is substantial. To minimize carbon loss, volatile fatty acids (VFAs) can be utilized as a raw material for bioalcohol production. Thus, a two-step chemical upgrading of VFAs into C2-5 alcohols is summarized in comparison with current challenges of biological fermentation processes for bioalcohol production. This review also provides the prospect of the hybrid biological/chemical process, presenting the technical advantages of the system. Finally, economic viability of hybridized process for bioalcohol production is compared with the current biological process.2020-09-01T00:00:00ZSSumM: Sparse Summarization of Massive GraphsLee, KyuhanJo, HyeonsooKo, JihoonLim, SungsuShin, Kijunghttp://hdl.handle.net/10203/2749992020-06-29T08:20:17Z2020-08-24T00:00:00ZTitle: SSumM: Sparse Summarization of Massive Graphs
Authors: Lee, Kyuhan; Jo, Hyeonsoo; Ko, Jihoon; Lim, Sungsu; Shin, Kijung
Abstract: Given a graph G and the desired size k in bits, how can we summarize G within k bits, while minimizing the information loss?
Large-scale graphs have become omnipresent, posing considerable computational challenges. Analyzing such large graphs can be fast and easy if they are compressed sufficiently to fit in main memory or even cache. Graph summarization, which yields a coarse-grained summary graph with merged nodes, stands out with several advantages among graph compression techniques. Thus, a number of algorithms have been developed for obtaining a concise summary graph with little information loss or equivalently small reconstruction error. However, the existing methods focus solely on reducing the number of nodes, and they often yield dense summary graphs, failing to achieve better compression rates. Moreover, due to their limited scalability, they can be applied only to moderate-size graphs.
In this work, we propose SSumM, a scalable and effective graph-summarization algorithm that yields a sparse summary graph. SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle. Compared with state-of-the-art competitors, SSumM is (a) Concise: yields up to 11.2X smaller summary graphs with similar reconstruction error, (b) Accurate: achieves up to 4.2X smaller reconstruction error with similarly concise outputs, and (c) Scalable: summarizes 26X larger graphs while exhibiting linear scalability. We validate these advantages through extensive experiments on 10 real-world graphs.2020-08-24T00:00:00ZStructural Patterns and Generative Models of Real-world HypergraphsDo, Manh TuanYoon, Se-eunHooi, BryanShin, Kijunghttp://hdl.handle.net/10203/2749982020-06-29T08:20:14Z2020-08-24T00:00:00ZTitle: Structural Patterns and Generative Models of Real-world Hypergraphs
Authors: Do, Manh Tuan; Yoon, Se-eun; Hooi, Bryan; Shin, Kijung
Abstract: Graphs have been utilized as a powerful tool to model pairwise relationships between people or objects. Such structure is a special type of a broader concept referred to as hypergraph, in which each hyperedge may consist of an arbitrary number of nodes, rather than just two. A large number of real-world datasets are of this form - for example, list of recipients of emails sent from an organization, users participating in a discussion thread or subject labels tagged in an online question. However, due to complex representations and lack of adequate tools, little attention has been paid to exploring the underlying patterns in these interactions.
In this work, we empirically study a number of real-world hypergraph datasets across various domains. In order to enable thorough investigations, we introduce the multi-level decomposition method, which represents each hypergraph by a set of pairwise graphs. Each pairwise graph, which we refer to as a k-level decomposed graph, captures the interactions between pairs of subsets of k nodes. We empirically find that at each decomposition level, the investigated hypergraphs obey five structural properties. These properties serve as criteria for evaluating how realistic a hypergraph is, and establish a foundation for the hypergraph generation problem. We also propose a hypergraph generator that is remarkably simple but capable of fulfilling these evaluation metrics, which are hardly achieved by other baseline generator models.2020-08-24T00:00:00ZIncremental Lossless Graph SummarizationKo, JihoonKook, YunbumShin, Kijunghttp://hdl.handle.net/10203/2750002020-06-29T08:20:19Z2020-08-24T00:00:00ZTitle: Incremental Lossless Graph Summarization
Authors: Ko, Jihoon; Kook, Yunbum; Shin, Kijung2020-08-24T00:00:00Z