Information Diffusion at Workplace

Zhang, Jiawei; Yu, Philip S.; Lv, Yuanhua; Zhan, Qianyi

doi:10.1145/2983323.2983848

Cited by 16 publications

(30 citation statements)

References 18 publications

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“…Translation based Graph Homogeneous Network Heterogeneous Network Property Network Embedding Embedding [3], [19], [11] Embedding [14], [18], [6] Embedding [4], [5] target network emerging regular regular regular network attributed heterogeneous multi-relational homogeneous heterogeneous #network multiple single single single proximity meta proximity first order first/second order/random walk first order [4], meta path [5] multi-source fusion anchor link based fusion N/A N/A N/A [30], [24], [26], [23], [29], [27], [28]. In the real world, on the same information entities, e.g., social media users [25], [21], [30], [24], movie knowledge library entries [31] and employees in companies [26], [23], [29], [27], [28], a large amount of information can actually be collected from various sources. These sources are usually of different varieties, like Foursquare vs Twitter [25], [21], [30], [24], IMDB vs Douban Movie sites [31], Yammer vs company organizational chart [26], [23], [29], [27], …”

Section: Aligned Heterogeneousmentioning

confidence: 99%

BL-MNE: Emerging Heterogeneous Social Network Embedding Through Broad Learning with Aligned Autoencoder

Zhang¹,

Xia

Zhang

et al. 2017

2017 IEEE International Conference on Data Mining (ICDM)

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Network embedding aims at projecting the network data into a low-dimensional feature space, where the nodes are represented as a unique feature vector and network structure can be effectively preserved. In recent years, more and more online application service sites can be represented as massive and complex networks, which are extremely challenging for traditional machine learning algorithms to deal with. Effective embedding of the complex network data into lowdimension feature representation can both save data storage space and enable traditional machine learning algorithms applicable to handle the network data. Network embedding performance will degrade greatly if the networks are of a sparse structure, like the emerging networks with few connections. In this paper, we propose to learn the embedding representation for a target emerging network based on the broad learning setting, where the emerging network is aligned with other external mature networks at the same time. To solve the problem, a new embedding framework, namely "Deep alIgned autoencoder based eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link and attribute in a unified analytic based on broad learning, and introduces the multiple aligned attributed heterogeneous social network concept to model the network structure. A set of meta paths are introduced in the paper, which define various kinds of connections among users via the heterogeneous link and attribute information. The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network. Extensive experiments have been done on real-world aligned social networks, which have demonstrated the effectiveness of DIME in learning the emerging network embedding vectors.

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Section: Aligned Heterogeneousmentioning

confidence: 99%

BL-MNE: Emerging Heterogeneous Social Network Embedding Through Broad Learning with Aligned Autoencoder

Zhang¹,

Xia

Zhang

et al. 2017

2017 IEEE International Conference on Data Mining (ICDM)

Self Cite

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“…In companies [16,15,17], employees usually have professional skills and expertise at different proficiency levels. Generally, senior employees who have gotten trained for a long time will know many skills very thoroughly.…”

Section: Terminology Definitionmentioning

confidence: 99%

“…Request permissions from Permissions@acm.org. tional settings [16,15,17]. Providing necessary professional career training for employees is very common in companies, which can help novice employees at elementary levels to master necessary working skills under the instructions of people from a high professional practitioner position.…”

Section: Introductionmentioning

confidence: 99%

“…Besides adopting professional services from external career training agencies, companies themselves can also train employees by involving them in projects carried out in the companies [16,15,17]. By collaborating with other employees to finish projects, employees can learn necessary professional skills from each other, which will effectively reduce the unnecessary expenditure and is also more practical for companies.…”

Section: Introductionmentioning

confidence: 99%

“…The ET problem is an important yet interesting problem. Besides training employees in companies [16,15,17], it can also be applied in many other concrete real-world problems. For instance, we can educate students in schools by involving them into study groups to do homework and projects together.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enterprise Employee Training via Project Team Formation

Zhang

2017

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

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Professional career training for novice employees at elementary levels to help them master necessary working skills is critical for both achieving employees' professional success and enhancing the enterprise growth. Besides adopting professional services from external career training agencies, companies can actually train the employees more effectively by involving them in various internal projects carried out in the companies. In this paper, we will study the "Employee Training" (ET) problem by assigning the employees to various concrete company internal projects. From the company perspective, besides training the employees, another important objective of carrying out these projects is to finish them successfully. The successful accomplishment of projects depends on various issues, like the skill qualification of the built teams and the effective collaboration among the team members. To achieve these two objectives simultaneously, a novel framework named "Team foRmAtion based employee traINing" (TRAIN) is proposed in this paper. TRAIN formulates the ET problem as a joint optimization problem, where the objective function considers the employees' overall skill gain and the team internal communication costs at the same time. To ensure the success of the projects, a new team skill qualification constraint is proposed and added to the optimization problem. Extensive experiments conducted on the real-world enterprise employee project team dataset demonstrate the effectiveness of TRAIN in addressing the problem.

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Frontier and Future Directions

Zhang

2019

Broad Learning Through Fusions

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Information Diffusion at Workplace

Cited by 16 publications

References 18 publications

BL-MNE: Emerging Heterogeneous Social Network Embedding Through Broad Learning with Aligned Autoencoder

BL-MNE: Emerging Heterogeneous Social Network Embedding Through Broad Learning with Aligned Autoencoder

Enterprise Employee Training via Project Team Formation

Frontier and Future Directions

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