ScholarWorks@Hanyang University: Kim, Sang-Wook

Detailed Information

Kim, Sang-Wook (김상욱)

COLLEGE OF ENGINEERING (SCHOOL OF COMPUTER SCIENCE)

Research Area: 데이타베이스, 데이타마이닝, 사회연결망 분석

Tel 02-2220-1736
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ResearcherID ORCiD Scopus Google Scholar ID ResearchGate

Keyword

Collaborative filtering:20||Blog:16||Blogosphere:16||Recommender systems:11||Data mining:10||블로그:9||Data Mining:9||GPU:8||Information diffusion:7||Social network analysis:7||데이터 마이닝:7||data mining:7||collaborative filtering:7||Information Diffusion:6||community detection:6||Similarity:6||recommender systems:6||Recommender system:6||classification:6||timeseries databases:6||Social Network Analysis:6||knearest neighbor query:5||graph embedding:5||Trust prediction:5||Clustering:5||s distance:5||social network analysis:5||Belief propagation:4||사회연결망 분석:4||scientific papers:4||성능 평가:4||Collaborative processing:4||linkbased similarity:4||Trust:4||False reputation:4||OLAP:4||Data imputation:4||SSD:4||Intelligent SSD:4||Data sparsity:4||information diffusion:4||Performance evaluation:4||SimRank:4||추천 시스템:4||Moving Object:4||Post ranking algorithm:4||PIVOT:4||Linkbased similarity:4||Hierarchical clustering:4||TopN recommendation:4||trust:3||centrality:3||데이터마이닝:3||정보 파급:3||블로고스피어:3||moving objects:3||Category:3||Recommendation system:3||road network:3||belief propagation:3||포스트 랭킹 알고리즘:3||Oneclass collaborative filtering:3||recommendation systems:3||clustering:3||matrix factorization:3||signed networks:3||Paper recommendation:3||similarity search:3||Static analysis:3||balance theory:3||malware classification:3||data sparsity:3||Content:3||블로그 공간:3||Trust network:3||클러스터링:3||Robustness:3||Duplicate document detection:3||subsequence matching:3||learningtorank:3||Blog action:3||recommender system:3||Single machine:3||graphbased outlier detection:3||Linkbased similarity measure:3||이동 객체:3||Graph engine:3||multimedia recommendation:3||Unfair ratings:3||Sparse matrix multiplication:3||multimedia information retrieval:3||feature extraction:3||Birthmark:3||Performance Evaluation:3||분류:3||Graph engines:3||GPUbased processing:2||Zeroinjection:2||Linkbased clustering:2||Rendering:2||블로그 액션:2||largescale graphs analysis:2||latent dirichlet allocation:2||adversarial learning:2||LinkClus:2||사회연결망분석:2||Literature Ranking Method:2||Road network:2||소셜 네트워크 분석:2||Literature Search Engine:2||linkbased similarity measures:2||Generative adversarial networks:2||Dimensionality Reduction:2||Rule Discovery:2||Graph:2||malware:2||malware author groups:2||Attackers:2||SDMiner:2||Malware classification:2||악성코드:2||balanced triangle:2||message passing:2||benchmark:2||Scheduling:2||Scientific Literature Database:2||nearest neighbor graph:2||blogosphere:2||network analysis:2||network embedding:2||blogs:2||bowtie:2||branchandbound:2||Scientific papers:2||Search engine:2||centerproximity:2||Graph mining:2||정보 파급 모델:2||정보파급:2||Shader:2||Graph sampling:2||Crowdsourcing:2||CRank:2||MovieLens:2||performance evaluation:2||performance improvement:2||Similarity Measurement:2||physical database design:2||Similarity analysis:2||텍스트 기반 유사도:2||DNA database:2||content:2||Simulatorbased evaluation:2||Multidimensional Indexing:2||Multidimensional Data:2||pricecomparison services:2||Group recommendation:2||Multimedia Information Retrieval:2||probabilistic model:2||DOT index:2||Heterogeneous:2||query processing:2||conversational recommender systems:2||협업 필터링:2||A* algorithm:2||indexing:2||random walk with restart:2||Heterogeneous scheduling:2||deep learning:2||Data Warehouse:2||Content Power User:2||dimensionality reduction (334):2||directed graph embedding:2||Earth mover&apos:2||distributed deep learning:2||diversification:2||Software theft detection:2||scientific literature:2||Data layout:2||Spacefilling curve:2||seminal paper finding:2||sequence database:2||Efficiency:2||Electric vehicle:2||signed network:2||signed network embedding:2||similar sequence matching:2||similarity:2||similarity computation:2||similarity measure:2||Content and link ranking:2||ecommerce:2||Implicit feedback:2||social network:2||earth mover&apos:2||Online social network:2||Data partitioning:2||Spatial join:2||Influence maximization:2||epinions:2||Spatiotemporal Index:2||Stock databases:2||tensor factorization:2||Information Diffusion Model:2||text based similarity:2||Execution time estimation:2||Citations:2||feature selection:2||flash device:2||TPR*trees:2||topn recommendation:2||Instagram:2||future prediction:2||traffic speed prediction:2||Parameterinsensitive:2||Explicit feedback:2||trustworthy graph convolutional networks:2||uFLIP:2||uninteresting items:2||generative adversarial networks:2||J2ME:2||Java:2||vector space model:2||Java virtual machine:2||graph mining:2||Job matching:2||TimeSeries Data:2||Job recommendation:2||Extraction of Blog Communities:2||Database organization:2||FARM:2||Collaborative Filtering:2||Trajectory:2||Trajectory Data:2||high dimensional indexing:2||Trust formation:2||Query Processing:2||Trust propagation:2||RWR:2||Random Walk with Restart:2||hybrid recommendation:2||Random walk:2||Densification power law:2||Range Query:2||impact factor:2||Accuracy:2||Twitter:2||Flash memory:2||Flash memory DBMS:2||index interpolation:2||Latent communities:2||Differential File:2||Recommendation:2||Contribution score:2||WCP:2||랜덤워크 위드 리스타트:2||Recommender Systems:2||kLASL:2||Web information retrieval:2||메시지 전달 기법:2||Recommending Stock Investments:2||Spatial data:1||Spatial data mining:1||Spatial database:1||Spatial databases:1||Spatial index:1||Spatial indexing:1||Spatiotemporal data:1||Static objects:1||Structural disparity:1||Subgroup Analysis:1||Subsequence Matching:1||Subsequence matching:1||Suffix tree:1||TPR*tree:1||TPR*트리:1||TPRtree:1||TV channel matching:1||TV show recommendation:1||Tags:1||Telematics:1||Temporal knowledge graph:1||Textbased Similarity Measure:1||Textbased similarity:1||Time Interval:1||Time Warping:1||Time complexity:1||Time interval:1||Timeseries:1||Timeseries analysis:1||Timestamp:1||TopN Recomendation:1||Tour schedule:1||Traffic speed prediction:1||Trust Network:1||Trust prediction model:1||Trust score prediction:1||Trustaware recommendation:1||Trustaware recommendations:1||Trustmessage passing:1||Trustworthiness:1||Tv show recommendation:1||URL string:1||URL 문자열:1||Uninteresting items:1||User behavior analysis:1||User interaction:1||User modeling:1||Video search:1||Virtual negative edges:1||Waiting time:1||Watchable episode:1||Watchable interval:1||Web Page Ranking:1||Web pages:1||Wind power:1||a GPUbased singlemachine graph engine. We design the core components required for GPUbased graph processing and incorporate them into the architecture of RealGraph. Further:1||abuser:1||active SSD:1||adamic/adar:1||adversarial training:1||android applications:1||api호출:1||approximate indexing:1||approximation:1||apps data mining:1||association rule mining:1||attacks:1||augmented attributes:1||author dominance:1||autoencoder:1||auxiliary features:1||balanced news consumption:1||bayesian personalized rank:1||benchmark datasets:1||big data:1||big data mining:1||blog:1||blog networks:1||breadth first search:1||buffering scheme:1||business models:1||centralized training:1||centreproximity:1||characterization:1||citation:1||citation analysis:1||citations:1||clicklog:1||clustering affinity:1||coldstart problems:1||commonidentity theory:1||community detection algorithms:1||community reinforcement:1||comparisonshopping services:1||consistency:1||consisting of vertices and edges:1||content power users (CPUs):1||contentbased image retrieval:1||contentbased information retrieval:1||contentcentric network:1||contextaware attention network:1||contribution based ranking:1||contribution constraints:1||contribution score:1||data imputation:1||data locality:1||data parallelism:1||database system:1||deeplearning model:1||degreerelated distributional shifts:1||dialogue:1||diffusionbased detection:1||dimensionality reduction:1||distance function:1||diversity tendency:1||document search:1||document similarity:1||domainexpert:1||dynamic analysis:1||dynamic classification:1||dynamic signed networks:1||emarketplace:1||earth mover:1||echo chamber:1||echo chamber effect:1||efficiently processes storagetomemory I/Os by considering unique characteristics of realworld graphs. Via an indepth analysis of RealGraph:1||egonetwork:1||electronic health records (EHR) graph:1||emergency management:1||epidemic response network:1||event sequence:1||exact match:1||execution time:1||expert:1||explanation.:1||explicit feedback:1||e마켓플레이스:1||fairness:1||fake news detection:1||feature representation learning:1||flash memory DBMS:1||flash storage:1||fraud detection:1||futuretime queries:1||gSpan:1||garbage collection:1||garbage collector:1||gateway cache:1||generative adversarial nets:1||graph anomaly detection:1||graph convolutional network:1||graph embedding methods:1||graph engine:1||graph processing:1||graph reformulation:1||graph structure:1||graphbased inference:1||group write:1||has been widely adopted for network analysis. Recently:1||hashtags:1||health informatics:1||heterogeneous:1||heterogeneous environments:1||heterogeneous systems:1||heuristic routing algorithm:1||hierarchical attention networks:1||highdimensional indexing:1||home controller:1||horizontal tables:1||however:1||humanintheloop approach:1||humanintheloop method:1||hybrid data fetch:1||hypergraph:1||hypergraph expansion:1||image retrieval:1||implicit feedback:1||imputation:1||in this paper:1||instorage processing:1||incentivized/penalized user model:1||inductive ability:1||influence maximization:1||information diversity:1||information loss:1||instagram:1||intercommunity edges:1||interactive classification:1||interest similarity:1||intracommunity edges:1||kNearestneighbor query:1||kmismatch:1||knearest neighbor queries:1||k최근접 이웃 질의:1||keyword set expansion:1||knowledge distillation:1||knowledge graph embedding:1||language model:1||large batch training:1||largescale sparse matrix:1||layerwise approach:1||learning rate scaling:1||like network:1||link prediction:1||linkbased measure:1||linkbased similarity measure:1||listwise loss:1||location awareness:1||locationbased services:1||longterm traffic prediction:1||malicious document:1||malware authors:1||malware detection:1||manifest information:1||many graph engines have been studied to efficiently process largescale realworld graphs. RealGraph:1||medication recommendation:1||memory management:1||microbenchmark:1||mobile public transportation system:1||modalityspecific properties:1||multidimensional index:1||multidimensional indexes:1||multimedia indexing:1||negative links:1||network alignment:1||network polarization:1||network privacy preserving:1||network traffic:1||networkbased inference:1||news recommendation:1||news recommender system:1||nodepairs similarity:1||nontransactional databases:1||one of the stateoftheart singlemachinebased graph engines:1||overlapping community detection:1||pagerank:1||pairwise learning:1||pairwise normalization:1||pairwise normalization problem:1||paper genealogy construction:1||parafac decomposition:1||parallelism:1||parameterfree:1||peak load reduction:1||performance:1||personalization:1||personalized node ranking:1||phishing URL detection:1||phising detection:1||political stance prediction:1||political stances:1||postuse preference:1||power schedule:1||power users:1||preuse preference:1||precision switching:1||preprocessing:1||privacy:1||privacy breach:1||propensity determination:1||quantization:1||query transformation:1||random walk:1||range query:1||ranking:1||ranking algorithm:1||rating:1||rating separation:1||realworld graphs:1||recommendation:1||recovery:1||recursive measure:1||relational algebra:1||reputation:1||reputation system:1||response time:1||reviews:1||road network databases:1||robustness:1||route information retrieval:1||segmentation:1||semantic similarity:1||sequential pattern:1||shapebased retrieval:1||shortest path:1||similarity score:1||single machine:1||smart grid:1||social media images:1||social networks:1||solidstate drives:1||sparse matrix multiplication:1||spatio:1||spatiotemporal data:1||spatiotemporal databases:1||spatiotemporal index:1||static analysis:1||string matching:1||structural:1||structural disparity:1||svd:1||temporal indexing:1||temporal knowledge graph:1||text analysis:1||textbased measure:1||textbased similarity measure:1||time warping:1||time warping distance:1||timeseries analysis:1||timestamped event sequence matching:1||topN recommendation:1||topk recommendation:1||topn 추천:1||topicoriented analysis:1||traffic flow forecasting:1||trajectory data:1||trust networks:1||trust prediction:1||trust prediction model:1||unfair ratings:1||user affinity:1||user behavior analysis:1||user experiment:1||user interaction:1||vehicular telematics network:1||video matching:1||visual hints:1||we found that there is still a chance for more performance improvement in the computation part of RealGraph despite its great I/O processing ability. Motivated by this:1||we propose RealGraphGPU:1||we propose two optimizations that successfully address the technical issues that could cause the performance degradation in the GPUbased:1||web search engines:1||webbased graph analysis:1||wildcard match:1||with the increasing size of realworld networks:1||가비지 컬렉션:1||가비지 컬렉터:1||가짜뉴스 탐지:1||개인화:1||건강 정보학:1||검색 엔진:1||계층적 클러스터링:1||공간 데이터:1||공간 데이터 마이닝:1||관심사 유사도:1||궤적:1||궤적 데이터:1||규칙 탐사:1||그래프 기반 아웃라이어 검출:1||그래프 임베딩:1||그래프 추론:1||근로자집단:1||논문 검색 엔진:1||논문 데이터베이스:1||논문 랭킹:1||논문 랭킹 방법:1||다양성 성향:1||다양화:1||다이얼로그:1||다차원 데이터:1||다차원 색인:1||대표 객체:1||대화형추천시스템:1||데이타 마이닝:1||데이터 웨어하우스:1||데이터 임퓨테이션:1||데이터 희소성:1||데이터베이스:1||데이터베이스 구조화:1||도로 네트워크:1||딥 러닝:1||랭킹 알고리즘:1||레코드 관리 방법:1||링크 기반 유사도:1||링크 기반 클러스터링:1||멀티미디어 인덱싱:1||멀티미디어 정보검색:1||메모리 관리:1||모듈:1||물리적 데이타베이스 설계:1||미래 예측:1||배아 데이터:1||버스마크:1||벡터 공간 모델:1||블로그 커뮤니티 추출:1||블로그월드:1||비디오 검색:1||비디오 매칭:1||사용자 상호작용:1||사용자 친밀성:1||서브시퀀스 매칭:1||성능 개선:1||성향 판정:1||소셜 네트워크:1||소셜 컨텍스트 기반 탐지:1||소프트웨어 유사도:1||소프트웨어버스마킹시스템:1||스트링 매칭:1||시각 정보:1||시계열 데이타베이스 서브시스 매칭:1||시계열 데이터:1||시계열 분석:1||시공간 인덱스:1||신뢰:1||신뢰 관계 예측 모델:1||신뢰 형성:1||신뢰예측:1||아웃라이어 검출:1||언어모델:1||연관 규칙:1||연관성 파급 모델:1||영역 질의:1||영향력 최대화:1||웹 검색 엔진:1||웹 페이지 랭킹:1||윈도우 실행파일:1||유사검색:1||유사도 측정 함수:1||유사도 함수:1||의약품 추천:1||이고네트워크:1||이미지 데이터 클러스터링:1||이익극대화:1||익스플리싯 피드백:1||인덱스 보간법:1||인덱싱:1||인덱싱 기법:1||임팩트팩터:1||자바:1||자바 가상머신:1||잠재적인 참조:1||전문가네트워크:1||전자 건강 기록(EHR) 그래프:1||전자 상거래:1||접두어 질의:1||주식 투자 추천:1||중복문서 검출:1||중복문서검출:1||중심 근접도:1||중심도:1||중첩 커뮤니티 탐색:1||지식증류기법:1||질의 처리:1||차등 파일:1||차원 축소:1||추천:1||추천시스템:1||카테고리:1||커뮤니티 탐색:1||커뮤니티 탐지 기법:1||컨테이너:1||컨텐츠 파워 유저:1||코로나바이러스:1||키워드 확장:1||타임 워핑:1||텐서 분해:1||트위터:1||특성 분석:1||특징 선택:1||팀구성문제:1||파급:1||파급 파워 유저:1||평점 분리:1||평판 시스템:1||플래시 메모리 DBMS:1||피싱 URL 탐지:1||학술 논문:1||학술 데이터베이스:1||행렬 분해:1||확률 모델:1||A* 알고리즘:1||AFNx01 algorithm:1||API calls:1||AUC:1||Abstract A graph:1||Academic literature data:1||Adversarial learning:1||Age and gender:1||Alternating least squares:1||Application IO trace:1||Approximate indexing:1||Approximate subsequence search:1||Artificial neural network:1||Association Rules:1||Attention network:1||Author group identification:1||Authority:1||Autoencoders:1||Automatic weighting:1||Autonomic level:1||Autonomic machine learning platform:1||Bayesian personalized ranking:1||Big data mining:1||Binary analysis:1||Blog communities:1||Blog data:1||Blog network:1||Blogaction:1||Buffer scheme:1||COVID19 dataset:1||CUDA:1||CVM:1||Caching:1||Characterization:1||Charging plan:1||Checkpointing:1||Citation:1||Classification:1||Collaborating Filtering:1||Collaborative approach:1||Community detection:1||Community extraction:1||Community reinforcement:1||Communitycascading:1||Comparison analysis:1||Container:1||Contentbased information retrieval:1||Context awareness:1||Contextawareness:1||Continuous query processing:1||Contribution based ranking:1||Contribution constraints:1||Cooperative computing:1||Coronavirus:1||Cosine similairty:1||Coverage:1||CrossAssociation:1||Crossassociation:1||Crowdsourced promotion:1||DBMS:1||Data Imputation:1||Data augmentation:1||Data locality:1||Data structures:1||Dataintensive algorithms:1||Database:1||Database recovery:1||Deep learning:1||Detection:1||Diffusion Power User:1||Diffusion model:1||Digital bibliographical service:1||Dimensionality reduction:1||Directed graphs:1||Directed networks:1||Distributed indexing:1||Distrust Intransitivity:1||Diversification:1||Domain Characteristics:1||Dynamic analysis:1||Dynamic ranking:1||Ecommerce:1||Emarketplace:1||Erecruitment services:1||Erecruitment sites:1||Earth Mover&apos:1||Economic activities:1||Embedded operating system:1||Embedding propagation:1||Emotion analysis:1||Epinions:1||Expected update time:1||Expert:1||Explainable recommendation:1||F1 score:1||Frelationship:1||Fake News Detection:1||Feature extraction:1||Fiverr:1||Flash storage:1||Fraud detection:1||Free space list:1||Garbage collection:1||Geocascading:1||Graph bag classification:1||Graph convolutional networks:1||Graph embedding:1||Graph neural network:1||Graph theory:1||Graphbased classification:1||Graphbased recommendations:1||Groundwater:1||Group management:1||Group write:1||Heterogeneous graph:1||Hierarchical Clustering:1||High performance:1||Highdimensional indexing:1||Hot data clustering:1||Hybrid approach:1||IO traces:1||Image data clustering:1||Image retrieval:1||Imagebased emotion analysis:1||In storage processing:1||Indexing Technique:1||Influence maximisation:1||Information diffusion model:1||Information fusion:1||Information retrieval:1||Intercommunity edges:1||Intracommunity edges:1||Knearest neighbor queries:1||Knearest neighbor query:1||Keyword extension:1||Keywordextension:1||Knowledge distillation:1||Knowledge graph:1||Korean blogosphere:1||LDA:1||LSH:1||Latent Citation:1||Latest News Articles:1||Licenses:1||Like Network:1||Likes:1||Linear algebra:1||Literature Scientific Ranking:1||Locationbased social networks:1||MERS:1||Machine learning:1||Malware:1||Matrix Factorization:1||Matrix factorization:1||Matrix multiplication:1||Message passing:1||Metric learning:1||MinHash:1||Mobile edge caching:1||Mobile smart devices:1||Model compression:1||MonteCarlo simulations:1||Moving object:1||Moving objects:1||Multidimensional indexing:1||Multimedia information retrieval:1||Multiple destinations:1||Music recommendation:1||Nearest neighbor retreival:1||Nearest neighbor search:1||Negative sampling:1||Network embedding:1||Network privacy preserving:1||Network traffic:1||New item recommendation:1||News Articles:1||News Recommendation:1||Nonclustering:1||Normalization:1||Obfuscated diverse attacks:1||Online communities:1||Online social networks:1||Outlier detection:1||PARAFAC decomposition:1||PINTRUST:1||PSbased distributed training:1||Pairwise preferences:1||Pairwise Normalization Problem JacSim:1||Pairwise normalization problem:1||Paper database:1||Paper genealogy construction:1||Parameterfree:1||Pearson correlation coefficient:1||Performance:1||Philosophical considerations:1||Positioning accuracy:1||Post Ranking Algorithms:1||Powerlaw degree distribution:1||Preference Model:1||Prefix Querying:1||Preprocessing:1||Privacy:1||Privacy breach:1||Promotion effects:1||Propagation:1||Random walk with restart:1||Ranking algorithm:1||Rating separation:1||Reachability vector:1||Realworld graph:1||Reciprocal trust relationship:1||Recommendation Method:1||Recommendation System:1||Recommendation systems:1||Record management:1||Record management method:1||Relevance Propagation:1||Representative objects:1||Reputation system:1||Response Network:1||Road network databases:1||Robust Features:1||Robust features:1||Robust recommender systems:1||Rule discovery:1||Rule matching:1||SDminer:1||SVD:1||Scientific Literature:1||Scientific literature:1||Selfsupervised learning:1||Sentiment analysis:1||Sequential pattern:1||Shader search:1||Shading network:1||SimRank problems:1||SimRank variants:1||Similartrend searching:1||Similarity Search:1||Similarity computation:1||Similarity search:1||Singular value decomposition:1||Smart City:1||Smart TV:1||Smart grid city:1||Social Context Based Detection:1||Social Network:1||Social advertising:1||Social media:1||Social network:1||Social network service:1||Social networking (online):1||Social networks:1||Software plagiarism:1||Software plagiarism detection:1||Solidstate drive (SSD):1||Sparse matrices:1||Sparse network:1||Spatial Data:1||Spatial Data Mining:1||

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