異構(gòu)網(wǎng)絡(luò)中的社團(tuán)檢測(cè)算法研究及應(yīng)用
發(fā)布時(shí)間：2019-04-04 11:57
【摘要】：異構(gòu)信息網(wǎng)絡(luò)是一種擁有多種類型的結(jié)點(diǎn)與鏈接的復(fù)雜網(wǎng)絡(luò),這些結(jié)點(diǎn)與鏈接蘊(yùn)藏著豐富的語義信息,給當(dāng)前數(shù)據(jù)挖掘領(lǐng)域帶來了更多的研究機(jī)會(huì)與挑戰(zhàn)。近年來,研究者們針對(duì)異構(gòu)信息網(wǎng)絡(luò),分別在相似性度量、圖聚類、鏈路預(yù)測(cè)以及推薦等方向做出了許多成果。本文以異構(gòu)信息網(wǎng)絡(luò)為研究對(duì)象,主要在社團(tuán)檢測(cè)和推薦系統(tǒng)兩個(gè)方面進(jìn)行研究。傳統(tǒng)的異構(gòu)信息網(wǎng)絡(luò)中社團(tuán)檢測(cè)的方法主要有基于排序、基于路徑與多視角學(xué)習(xí)三種類型,前兩者多根據(jù)概率圖模型來求解模型,后者則主要利用多視角學(xué)習(xí)方法來解決異構(gòu)網(wǎng)絡(luò)中的問題。而基于異構(gòu)網(wǎng)絡(luò)的推薦系統(tǒng)則可以看做是基于多源信息融合后的推薦,主要以融合策略和融合信息來提高推薦性能。與之不同的是,本文以全新的角度(將異構(gòu)信息網(wǎng)絡(luò)挖掘轉(zhuǎn)化為同構(gòu)信息網(wǎng)絡(luò)挖掘)出發(fā),借助信息在元路徑上的有效傳播,提出一種分解技術(shù),能夠在無信息損失的前提下將原始異構(gòu)信息網(wǎng)絡(luò)分解為一系列同構(gòu)信息網(wǎng)絡(luò)。同時(shí)基于該分解策略,本文分別提出了一種異構(gòu)信息網(wǎng)絡(luò)的社團(tuán)檢測(cè)算法HomClus與一種融合用戶與項(xiàng)目信息的推薦方法CSR。這三者構(gòu)成了本文的核心內(nèi)容,本文的主要貢獻(xiàn)如下:第一、提出了異構(gòu)信息網(wǎng)絡(luò)的基于元路徑的分解策略。該策略主要利用元路徑反映實(shí)體間的不同關(guān)系的本質(zhì),針對(duì)目標(biāo)類型實(shí)體,通過簡(jiǎn)單的矩陣操作得到不同路徑下目標(biāo)類型實(shí)體的關(guān)系權(quán)重矩陣——也就是同構(gòu)信息網(wǎng)絡(luò)。且該過程對(duì)目標(biāo)類型而言沒有信息損失。因此,對(duì)異構(gòu)網(wǎng)絡(luò)的相關(guān)研究問題都可以簡(jiǎn)化為在目標(biāo)類型的同構(gòu)網(wǎng)絡(luò)上的研究問題,從而更容易被解決。第二、提出了基于異構(gòu)信息網(wǎng)絡(luò)的分解策略的社團(tuán)檢測(cè)算法HomClus。該方法在第一個(gè)貢獻(xiàn)成果的條件下,首先將異構(gòu)信息網(wǎng)絡(luò)轉(zhuǎn)化為一系列同構(gòu)信息網(wǎng)絡(luò),并整合為統(tǒng)一的網(wǎng)絡(luò)結(jié)構(gòu)。其次,使用非負(fù)矩陣分解快捷地將節(jié)點(diǎn)轉(zhuǎn)化為向量,即將整個(gè)網(wǎng)絡(luò)投影到低維子空間中。最后,采用高效的聚類方法如基于同步的聚類方法對(duì)低維子空間中的“節(jié)點(diǎn)”進(jìn)行聚類,從而檢測(cè)出原始網(wǎng)絡(luò)中潛在的社團(tuán)結(jié)構(gòu)。實(shí)驗(yàn)表明,HomClus算法與領(lǐng)域內(nèi)的前沿算法相比有很大的優(yōu)勢(shì),如算法直觀簡(jiǎn)潔,參數(shù)不敏感,同時(shí)也驗(yàn)證了異構(gòu)信息網(wǎng)絡(luò)分解策略的有效性與實(shí)用性。第三、提出了基于異構(gòu)信息網(wǎng)絡(luò)的分解策略的推薦算法CSR。該方法針對(duì)推薦系統(tǒng)中典型的實(shí)體對(duì)象——用戶與項(xiàng)目,利用異構(gòu)信息網(wǎng)絡(luò)的分解策略,將用戶的異構(gòu)信息、項(xiàng)目的異構(gòu)信息同時(shí)轉(zhuǎn)化為同構(gòu)信息。并受當(dāng)前較為流行的基于漸近因子模型的推薦方法與相似性正則化的啟發(fā),將用戶信息、項(xiàng)目信息以及評(píng)分信息三者以集體相似性正則化一致逼近的形式有效地融合在一起,最后產(chǎn)生高質(zhì)量的推薦結(jié)果。
[Abstract]:Heterogeneous information network is a complex network with many kinds of nodes and links. These nodes and links contain abundant semantic information, which brings more research opportunities and challenges to the current field of data mining. In recent years, researchers have made many achievements in similarity measurement, graph clustering, link prediction and recommendation for heterogeneous information networks. In this paper, heterogeneous information network as the research object, mainly in the community detection and recommendation system two aspects. The traditional methods of community detection in heterogeneous information networks are mainly based on ranking, path-based learning and multi-perspective learning, and the first two are based on probability graph model to solve the model. The latter mainly uses the multi-perspective learning method to solve the problems in heterogeneous networks. The recommendation system based on heterogeneous network can be regarded as a recommendation based on multi-source information fusion, mainly based on fusion strategy and fusion information to improve the performance of recommendation. In contrast, from a new perspective (transforming heterogeneous information network mining into isomorphic information network mining), this paper puts forward a decomposition technique with the help of the effective propagation of information in meta-path, which is based on the transformation of heterogeneous information network mining into isomorphism information network mining. The original heterogeneous information network can be decomposed into a series of isomorphic information networks without information loss. At the same time, based on this decomposition strategy, a community detection algorithm for heterogeneous information network (HomClus) and a recommendation method (CSR.) for fusion of user and project information are proposed in this paper. The main contributions of this paper are as follows: firstly, a meta-path-based decomposition strategy for heterogeneous information networks is proposed. This strategy mainly uses meta-path to reflect the essence of different relationships between entities. According to the object-type entity, the relation weight matrix of the target-type entity under different paths is obtained by simple matrix operation, that is, isomorphic information network. And this process has no loss of information for the target type. Therefore, the related research problems of heterogeneous networks can be simplified to the research problems on the target-type isomorphic networks, so that they can be solved more easily. Secondly, a community detection algorithm HomClus. based on the decomposition strategy of heterogeneous information network is proposed. Under the condition of the first contribution, the method firstly transforms the heterogeneous information network into a series of isomorphic information networks and integrates them into a unified network structure. Secondly, the non-negative matrix decomposition is used to quickly transform the nodes into vectors, that is, the whole network is projected into the low-dimensional subspace. Finally, efficient clustering methods, such as synchronization-based clustering, are used to cluster "nodes" in low-dimensional subspaces, so as to detect the potential community structure in the original network. Experiments show that the HomClus algorithm has great advantages over the frontier algorithms in the field, such as simple and intuitive algorithm and insensitive parameters. At the same time, it also verifies the effectiveness and practicability of the heterogeneous information network decomposition strategy. Thirdly, the recommendation algorithm CSR. based on the decomposition strategy of heterogeneous information network is proposed. Aiming at the typical entity object in recommendation system-user and project, this method uses the decomposition strategy of heterogeneous information network to transform the heterogeneous information of user and the heterogeneous information of project into isomorphic information at the same time. Inspired by the current popular recommendation method based on asymptotic factor model and similarity regularization, the user information, project information and scoring information are effectively fused together in the form of collective similarity regularization and uniform approximation. Finally, high-quality recommendations are produced.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O157.5
[Abstract]:Heterogeneous information network is a complex network with many kinds of nodes and links. These nodes and links contain abundant semantic information, which brings more research opportunities and challenges to the current field of data mining. In recent years, researchers have made many achievements in similarity measurement, graph clustering, link prediction and recommendation for heterogeneous information networks. In this paper, heterogeneous information network as the research object, mainly in the community detection and recommendation system two aspects. The traditional methods of community detection in heterogeneous information networks are mainly based on ranking, path-based learning and multi-perspective learning, and the first two are based on probability graph model to solve the model. The latter mainly uses the multi-perspective learning method to solve the problems in heterogeneous networks. The recommendation system based on heterogeneous network can be regarded as a recommendation based on multi-source information fusion, mainly based on fusion strategy and fusion information to improve the performance of recommendation. In contrast, from a new perspective (transforming heterogeneous information network mining into isomorphic information network mining), this paper puts forward a decomposition technique with the help of the effective propagation of information in meta-path, which is based on the transformation of heterogeneous information network mining into isomorphism information network mining. The original heterogeneous information network can be decomposed into a series of isomorphic information networks without information loss. At the same time, based on this decomposition strategy, a community detection algorithm for heterogeneous information network (HomClus) and a recommendation method (CSR.) for fusion of user and project information are proposed in this paper. The main contributions of this paper are as follows: firstly, a meta-path-based decomposition strategy for heterogeneous information networks is proposed. This strategy mainly uses meta-path to reflect the essence of different relationships between entities. According to the object-type entity, the relation weight matrix of the target-type entity under different paths is obtained by simple matrix operation, that is, isomorphic information network. And this process has no loss of information for the target type. Therefore, the related research problems of heterogeneous networks can be simplified to the research problems on the target-type isomorphic networks, so that they can be solved more easily. Secondly, a community detection algorithm HomClus. based on the decomposition strategy of heterogeneous information network is proposed. Under the condition of the first contribution, the method firstly transforms the heterogeneous information network into a series of isomorphic information networks and integrates them into a unified network structure. Secondly, the non-negative matrix decomposition is used to quickly transform the nodes into vectors, that is, the whole network is projected into the low-dimensional subspace. Finally, efficient clustering methods, such as synchronization-based clustering, are used to cluster "nodes" in low-dimensional subspaces, so as to detect the potential community structure in the original network. Experiments show that the HomClus algorithm has great advantages over the frontier algorithms in the field, such as simple and intuitive algorithm and insensitive parameters. At the same time, it also verifies the effectiveness and practicability of the heterogeneous information network decomposition strategy. Thirdly, the recommendation algorithm CSR. based on the decomposition strategy of heterogeneous information network is proposed. Aiming at the typical entity object in recommendation system-user and project, this method uses the decomposition strategy of heterogeneous information network to transform the heterogeneous information of user and the heterogeneous information of project into isomorphic information at the same time. Inspired by the current popular recommendation method based on asymptotic factor model and similarity regularization, the user information, project information and scoring information are effectively fused together in the form of collective similarity regularization and uniform approximation. Finally, high-quality recommendations are produced.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O157.5
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 張邦佐;桂欣;何濤;孫煥W，
本文編號(hào)：2453785
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